Intelligent Image Retrieval Techniques: A Survey
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paper_title: A self-relevance feedback method based on object labels

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User's relevence feedback is often included in many content-based image retrieval (CBIR) systems, and this method is proved to be effective in improving the retrieval result. However, it may cause too much user participation which may make users impatient. To solve this problem, the paper proposes a self-relevance feedback method for CBIR which needs no user involvement. Self-relevance is seldom mentioned in CBIR as it is usually difficult to increase the performance of a system. Based on the “concept occurrence vector” (COV) used for image retrieval, the proposed method can improve the precision of the retrieval process, which is proved by our experiments. Though the improvement is not very huge, the method make the application of self-relevance feedback in CBIR possible.
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paper_title: Mining Visual Associations from User Feedback for Weighting Multiple Indexes in Geospatial Image Retrieval

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Geospatial content-based image retrieval (CBIR) systems can be used to query for visually similar images by identifying similar patterns between a query image and those in the database. When several different classes of features are used, some queries require that each class should be given a different degree of weight; to this end, CBIR indexes are built for each class of features. This paper proposes an approach for weighting multiple indexes in a geospatial CBIR system by mining information from user feedback. After a small number of iterations of relevance feedback and data mining, index weights can be determined dynamically per query. Using this technique geospatial retrieval system precision of results increased from 70% to 79% after 5 iterations of feedback.
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paper_title: Using relevance feedback to reduce the semantic gap in content-based image retrieval of mammographic masses

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This paper presents the use of relevance feedback (RFb) to reduce the semantic gap in content-based image retrieval (CBIR) of mammographic masses. Tests were conducted where the radiologists' classification of the lesions based on the BI-RADS categories were used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon Zernike moments. The performance of CBIR was measured in terms of precision and recall of retrieval. The results indicate improvement due to RFb of up to 41.6% in precision. In our experiments, the gain in the performance of CBIR with RFb was associated with the BI-RADS category of the query mammographic image, with large improvement in cases of lesions belonging to categories 4 and 5. The proposed method could find applications in computer-aided diagnosis (CAD) of breast cancer.
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paper_title: Learning in content-based image retrieval

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In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR). First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap, to fit the unique nature of relevance feedback as a small sample biased classification problem. Secondly, a WARF (word association via relevance feedback) formula is presented for learning keyword relations during the process of relevance feedback. We also introduce our new user interface for CBIR, ImageGrouper, which is designed to support more sophisticated user feedbacks and annotations. Finally, we use the D-EM (Discriminant-EM) algorithm as a way of exploiting unlabeled data in CBIR and offer some insights as to when unlabeled data will help.
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paper_title: Learning semantics in content based image retrieval

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Content-based image retrieval (CBIR) is an attempt to remove the bottleneck of visual semantic understanding needed in automated indexing in visual information retrieval. However, the myth about the power of visual-feature-based indexing was quickly diminished as such features are far from representing semantic visual contents and producing meaningful indexes. One solution is to apply relevance feedback to refine queries or similarity measures in the search process and apply machine learning techniques to learn semantic annotations. In this paper, we address the key issues involved in relevance feedback of CBIR systems and review solutions to these issues. Based on these discussions, we present a relevance feedback and semantic learning framework for CBIR. We hope the ideas presented in this paper serve as a catalyst to more research efforts in this direction.
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paper_title: Automatic selection of attributes by importance in relevance feedback visualisation

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Relevance feedback visualisation (RFV) is a technique developed to visualise the feature values of returned results in a content-based image retrieval system that incorporates relevance feedback. RFV is used also to re-sort retrieved results according to user requirements, enable the interactive investigation of pertinent features and permit the discovery of otherwise unidentifiable trends in the dataset. When large numbers of features are involved, manually determining which feature attribute graphs are the most important can be a burdensome task. In this paper, a method for automatically sorting attribute graphs according to their significance in the search operation is introduced. The result is that features worthy of further investigation are immediately identified, the user interface is improved, and the CBIR system is made more effective.
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paper_title: Incorporating real-valued multiple instance learning into relevance feedback for image retrieval

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This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.
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paper_title: An Anamnestic Semantic Tree-Based Relevance Feedback Method in CBIR System

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Relevance feedback is a usually used technique to narrow the gap between high-level concepts and low-level visual features in the content-based image retrieval. In this paper, a novel long-term learning mechanism is proposed to grasp the retrieval intention as much as possible. With more retrieval sessions going on, an anamnesis semantic tree is constructed to record the semantic relationship between the query and the retrieved back images on the high level concepts. In the dynamic updating process of the anamnesis semantic tree, both the mean shift based query refining and clustering techniques are adopted. The final experimental results show that the proposed approach greatly improves the retrieval performance
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paper_title: An Adaptable Image Retrieval System With Relevance Feedback Using Kernel Machines and Selective Sampling

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This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, multiple mapping subsystems that adaptively modify the similarity measures, and a relevance feedback mechanism that incorporates user information. The adaptation process drives the retrieval error to zero in order to exactly meet either an existing multiclass classification model or the user high-level concepts using reference-model or relevance feedback learning, respectively. To facilitate the selection of the most informative query images during relevance feedback learning a new method based upon the Fisher information is introduced. Model-reference and relevance feedback learning mechanisms are thoroughly tested on a domain-specific image database that encompasses a wide range of underwater objects captured using an electro-optical sensor. Benchmarking results with two other relevance feedback learning methods are also provided.
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paper_title: Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples

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In content-based image retrieval, understanding the user's needs is a challenging task that requires integrating him in the process of retrieval. Relevance feedback (RF) has proven to be an effective tool for taking the user's judgement into account. In this paper, we present a new RF framework based on a feature selection algorithm that nicely combines the advantages of a probabilistic formulation with those of using both the positive example (PE) and the negative example (NE). Through interaction with the user, our algorithm learns the importance he assigns to image features, and then applies the results obtained to define similarity measures that correspond better to his judgement. The use of the NE allows images undesired by the user to be discarded, thereby improving retrieval accuracy. As for the probabilistic formulation of the problem, it presents a multitude of advantages and opens the door to more modeling possibilities that achieve a good feature selection. It makes it possible to cluster the query data into classes, choose the probability law that best models each class, model missing data, and support queries with multiple PE and/or NE classes. The basic principle of our algorithm is to assign more importance to features with a high likelihood and those which distinguish well between PE classes and NE classes. The proposed algorithm was validated separately and in image retrieval context, and the experiments show that it performs a good feature selection and contributes to improving retrieval effectiveness.
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paper_title: Polygonal Approximation of Contour Shapes Using Corner Detectors

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A great amount of corner detectors that appear in literature are based on using the Freeman chain code of eight directions, which is used to represent contour shapes. We propose a new method for corner detection based on a three-symbol chain code representation, which requires lower storage memory and an easy way to obtain shape corners. We compare it with five existing methods, which are well known in the literature, giving our method a better performance. Furthermore, in order to reconstruct the original shapes through polygonal approximations, we propose an error parameter to quantify the efficiency. This can be accomplished by considering the redundancy of points produced when looking for corners and when computing the difference between the original region and the approximated polygon.
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paper_title: Image retrieval using fuzzy relevance feedback and validation with MPEG-7 content descriptors

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Content-Based Image retrieval has emerged as one of the most active research directions in the past few years. In CBIR, selection of desired images from a collection is made by measuring similarities between the extracted features. It is hard to determine the suitable weighting factors of various features for optimal retrieval when multiple features are used. In this paper, we propose a relevance feedback frame work, which evaluates the features, from fuzzy entropy based feature evaluation index (FEI) for optimal retrieval by considering both the relevant as well as irrelevant set of the retrieved images marked by the users. The results obtained using our algorithm have been compared with the agreed upon standards for visual content descriptors of MPEG-7 core experiments.
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paper_title: An intelligent on-line system for content based image retrieval

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We propose an intelligent content based image retrieval system and it is an extension of a previously published research paper (S. Kulkarni et al., 1999), where a neuro-fuzzy technique was presented. The CBIR system will accept multiple queries as input such as "mostly red and many blue and few green" that can be provided online and the outputs of the system are the images with their confidential values. The system uses fuzzy logic to interpret multiple natural expressions such as mostly, many and few and a neural network to learn the meaning of mostly red, many red and few red. The system has been implemented on the World Wide Web using CGI scripts and C programming language. Previously (S. Kulkarni et al., 1999), we presented some preliminary results using a single query. The paper presents the extension of our online system for multiple queries such as "many blue and few green". A number of experiments using our online system is conducted and the results are included and discussed in the paper.
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paper_title: Spin Images and Neural Networks for Efficient Content-Based Retrieval in 3D Object Databases

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We describe a system for querying 3D model databases using the spin image representation as a shape signature for objects depicted as triangular meshes. The spin image representation facilitates the task of aligning the query object with respect to matched models (coarse-grain registration). The main contribution of this work is the introduction of a three-level indexing schema based on artificial neural networks. The indexing schema improves significantly the efficiency in matching query spin images against those stored in the database. Our results are suitable for content-based retrieval in 3D general object databases. A particular application to molecular databases is also presented.
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paper_title: Efficient target search with relevance feedback for large CBIR systems

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Recent content-based image retrieval (CBIR) techniques were designed around query refinement based on relevance feedback. They suffer from slow convergence, high disk I/O, and do not even guarantee to find intended targets. In this paper, we identify the cause of these problems and propose several efficient target search methods to address these drawbacks. Our complexity analysis shows that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We evaluated our techniques on large datasets in simulated and realistic environments. The results show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be used to improve the effectiveness of existing CBIR systems with relevance feedback.
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paper_title: Semi-supervised distance metric learning for collaborative image retrieval and clustering

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Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are limited and noisy. We formally formulate the learning problem into a convex optimization task and then present a new technique, named as “Laplacian Regularized Metric Learning” (LRML). Two efficient algorithms are then proposed to solve the LRML task. Further, we apply the proposed technique to two applications. One direct application is for Collaborative Image Retrieval (CIR), which aims to explore the CBIR log data for improving the retrieval performance of CBIR systems. The other application is for Collaborative Image Clustering (CIC), which aims to explore the CBIR log data for enhancing the clustering performance of image pattern clustering tasks. We conduct extensive evaluation to compare the proposed LRML method with a number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information. Encouraging results validate the effectiveness of the proposed technique.
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paper_title: A fuzzy logic based approach to feedback reinforcement in image retrieval

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Nowadays, due to the spread of digital imaging technologies, the design of effective content based image retrieval (CBIR) systems is perceived by the research community as a primary problem. Various techniques such as clustering and relevance feedback were proposed to obtain a certain level of knowledge about a given image database. Often clustering techniques were used to obtain a first level characterization of the image database used to speed up the successive stage of queries. In this work the authors use the knowledge obtained using a fuzzy clustering algorithm to reinforce the user feedback. The system was tested on the Columbia Coil-20 image database and the obtained results seem to be encouraging.
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paper_title: SOM-Based sample learning algorithm for relevance feedback in CBIR

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Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. In recent years, the relevance feedback scheme has been applied to Content-Based Image Retrieval (CBIR) and effective results have been obtained. However, most of the conventional feedback process has the problem that updating of metric space is hard to understand visually. In this paper, we propose a CBIR algorithm using Self-Organizing Map (SOM) with visual relevance feedback scheme. Then a pre-learning algorithm in the visual relevance feedback is proposed for constructing user-dependent metric space. We show the effectiveness of the proposed technique by subjective evaluation experiments.
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paper_title: Speeding up relevance feedback in image retrieval with triangle-inequality based algorithms

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A content-based image retrieval(CBIR) system has been constructed to integrate relevance feedback with triangle-inequality based algorithms. The system offers typically 20 to 30 times faster retrieving speed with minimum sacrifice of retrieval performance on Corel database consisting of more than 17,000 images. The theoretic framework is built by using triangle-inequality based algorithms at sub-feature level and using relevance feedback techniques at feature level. Results show retrieval performance is clearly improved over the approach with only triangle-inequality based algorithms. A new high level weight updating method for the hierarchical distance model for relevance feedback is proposed.
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paper_title: Relevance feedback based on query refining and feature database updating in CBIR system

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Relevance feedback (RF), which introduces human visual perception into the retrieval process gradually, is an efficient improvement for narrowing down the gap between low-level visual feature representation of an image and its semantic meaning in content-based image retrieval (CBIR). In this paper, a new relevance feedback approach based on query refining and feature database updating in CBIR system is proposed. To make the new query more representative for the query concept in each round of feedback iteration, a new query-refining scheme is put forth, which is based on the different contributions among positive samples to the formation of query concept. In similarity measure, a nonlinear exponential mapping for the coefficient corresponding to different feature component is adopted to reduce the bias, which is caused due to small number of user labeled samples. In addition, an updateable feature-database strategy is also proposed to gradually accumulate the useful semantic information from past rounds of feedback iteration for next round. We test the proposed algorithm on the Corel natural image database and the final experimental results show that the proposed approach greatly improves the retrieval performance.
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paper_title: A Comparison of Relevance Feedback Strategies in CBIR

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Relevance feedback (RF) is considered to be very useful in reducing semantic gap and thus enhancing accuracy of a Content-Based Image Retrieval system. In this paper, we have given a brief overview of research done in this area with an emphasis on feature re-weighting approach, a popular RF technique. We have also discussed an instance-based approach that has been introduced very recently. We considered image retrieval as a dichotomous classification problem and compared performances of the two RF strategies with four different datasets, with number of images ranging from 1000 to 19511.
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paper_title: Interactive CBIR using RBF-based relevance feedback for WT/VQ coded images

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Powerful interfaces provide great potential for retrieval systems to adapt to dynamic user needs and allow a more accurate modeling of image similarity from the users' point of view. We propose a novel method within the interactive framework. It allows the users to directly modify the system characteristics by specifying their desired image attributes in the form of training samples. More specifically, we have adopted a radial basis function (RBF) method for implementing an adaptive metric which progressively models the notion of image similarity through continual feedback from the users. The proposed approach has been integrated into an image retrieval system using images compressed by wavelet transform and vector quantization coders. Comparisons with some of the recent systems using the standard texture database indicate that the proposed method provides the more favorable retrieval result.
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paper_title: Semantic image retrieval using relevance feedback and reinforcement learning algorithm

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Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user's query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users' perceptions and reduce the gap between high-level image semantics and low-level image features. In the past 30 years, relevance feedback (RF) has been an effective query modification approach to improving the performance of information retrieval (IR) by interactively asking a user whether a set of documents are relevant or not to a given query concept. This paper aims at developing a scheme for intelligent image retrieval using machine learning technique and the information gathered from the user's feedback. This helps the system on the following rounds of the retrieval process to better approximate the present need of the user. We have shown that a powerful relevance feedback mechanism can be implemented by using reinforcement learning algorithms. The user thus does not need to explicitly specify weights for relationship between images and concepts, because the weights are formed implicitly by the system. The proposed relevance feedback technique is described, analyzed qualitatively, and visualized in the paper. Also, its performance is compared with a reference method. Experimental results demonstrate that our proposed technique is promising.
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paper_title: Combined automatic weighting and relevance feedback method in Content-Based Image Retrieval

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Relevance Feedback (RF) is a powerful technique in Content-Based Image Retrieval (CBIR) system and has become a very active research topic in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representation and ignored the subjectivity of human perception. There exists a gap between high-level concepts and low-level features. As an effective solution, the RF technique has been used on many CBIR systems to improve the retrieval precision. In this paper, a combined automatic weighting and relevance feedback method is proposed to improve the retrieval performance of CBIR. An approach using genetic algorithm for computing the initial weight of feature vector was introduced. By moving the query vector and updating the weighting factors simultaneously, the convergence speed of the relevance feedback retrieval is accelerated. Experimental results show that this method achieves high accuracy and effectiveness in CBIR.
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paper_title: A Novel Relevance Feedback Method in Content-Based Image Retrieval

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Relevance Feedback (RF) is a powerful technique in Content-Based Image Retrieval (CBIR) system and has become a very active research topic in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representation and ignored the subjectivity of human perception. There exists a gap between high-level concepts and low-level features. As an effective solution, the RF technique has been used on many CBIR systems to improve the retrieval precision. In this paper, a novel relevance feedback method is proposed to improve the retrieval performance of CBIR. By moving the query vector and updating the weighting factors simultaneously, the convergence speed of the relevance feedback retrieval is accelerated. Experimental results show that this method achieves high accuracy and effectiveness in CBIR.
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paper_title: Multiple boosting SVM active learning for image retrieval

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Content-based image retrieval can be viewed as a classification problem, and the small sample size leaning difficulty makes it difficult for most CBIR classifiers to get satisfactory performance. In this paper, using the SVM classifier as the component classifier, the method of ensemble of classifiers is incorporated into the relevance feedback process to alleviate this problem from two aspects: (1) within each feedback round, multiple parallel component classifiers are constructed, one over one feature subspace individually, and then are merged together to get an ensemble classifier; (2) during feedback rounds, a boosting method is incorporated to sequentially combine the component classifiers over each feature subspace respectively, which further improves the classification result. Experiments over 5000 images show that the proposed method can improve the retrieval performance consistently, without loss of efficiency.
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paper_title: A Semi-Supervised Learning Based Relevance Feedback Algorithm in Content-Based Image Retrieval

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As a useful solution for address the faultage between image features and semanteme, relevance feedback (RF) became an effective approach to boost image retrieval. In supervised-based machine learning algorithm, insufficient Labeled training data and the unlabeled data in one RF circle can not represent scatter of features space for all irrelevant images, such algorithm used for CBIR did not show a high performance. As a research hot point, semi-supervised, it can utilize unlabeled data to estimate model of RF so that boost the retrieval performance. This paper proposed a new algorithm for RF: make use of expectation maximization (EM) to learn RBF function for RBF neutral network, integrated active learning to void a local value EM learned, and reduce iterations of feedback, as a result this algorithm learned a RF model based on RBF. Experience indicated that: compare to EM and Bayes, efficiency of learner is improved, user's query concept is grasped quickly.
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paper_title: A neural network-based image retrieval using nonlinear combination of heterogeneous features

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In content-based image retrieval (CBIR), content of an image can be expressed in terms of different features such as color, texture, shape, or text annotations. Retrieval based on these features can be various by the way how to combine the feature values. Most of the existing approaches assume a linear relationship between different features, and the usefulness of such systems was limited due to the difficulty in representing high-level concepts using low-level features. In this paper, we introduce Neural Network-based Flexible Image Retrieval (NNFIR) system, a human-computer interaction approach to CBIR using Radial Basis Function (RBF) network to combine the values of the heterogeneous features. By using the RBF network, this approach determines nonlinear relationship between features so that more accurate similarity comparison between images can be supported. The experimental results show that the proposed approach captures the user's perception subjectivity more precisely using the dynamically updated weights.
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paper_title: Comparison of CBIR Systems with Different Number of Feature Vector Components

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Content-based image retrieval (CBIR) systems with user relevance feedback are considered. The influence of the type and the number of feature vector (FV) components on the retrieval efficiency was investigated. We compared a CBIR system with a very small number of FV components (only 25 components describing color and texture) with a system with a high-dimensional FV inspired by MPEG-7 (556 coordinates describing color, texture and line directions), as well as with a system using feature vector reduction (FVR) of about 90% (with about 50 FV components from the full-length 556-component FVs). The systems are tested over the annotated Corel IK and Corel 60K datasets. Simulation results show that a decreased number of FV components does not have significant influence on the quality of image retrieval, while the processing time is reduced compared to CBIR with full-length FV and/or FVR.
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paper_title: Fighting the Semantic Gap on CBIR Systems through New Relevance Feedback Techniques

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This paper introduces two novel relevance feedback techniques that integrate a new way to implement the query center movement with a suitable weighting on the similarity function. These techniques integrated to a content-based image retrieval (CBIR) system, improves the precision of the results when using texture features up to 42%, and employing at most 5 iterations. Thus, the user satisfaction with the system is increased as our experiments demonstrated. Besides being effective, the new RF techniques are very fast as they take less than one second to reprocess the queries at each iteration. The experiments also show that with three iterations the users are satisfied with the query results, and the major gain in precision happens in the first iteration, achieving improvements of up to 30%, what lessens the user efforts and anxiety.
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paper_title: Intelligent User Interface Agents in Content-Based Image Retrieval

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The ability to browse and retrieve images based upon image content is of rapidly growing importance. This paper describes a content-based image retrieval (CBIR) system that combines dynamic, user-driven search capabilities. The system's ability to support both query-by-sketch and query-by-example is enhanced by the use of intelligent user interface agents (UIAs). The UIAs use both neural networks and an expert reasoning system to assist with relevance feedback. In addition to describing the system, a new CBIR evaluation metric is presented
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paper_title: Graph-Cut Transducers for Relevance Feedback in Content Based Image Retrieval

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Closing the semantic gap in content based image retrieval (CBIR) basically requires the knowledge of the user's intention which is usually translated into a sequence of questions and answers (Q&A). The user's feedback to these questions provides a CBIR system with a partial labeling of the data and makes it possible to iteratively refine a decision rule on the unlabeled data. Training of this decision rule is referred to as transductive learning. This work is an original approach to relevance feedback (RF) based on graph-cuts. Training consists in implicitly modeling the manifold enclosing both the labeled and unlabeled dataset and finding a partition of this manifold using a min-cut. The contribution of this work is two-fold (i) this is the first comprehensive study of relevance feedback using graph cuts and (ii) our RF model exploits the structure of the data manifold by considering also the structure of the unlabeled data. Experiments conducted on generic as well as specific databases show that our graph-cut based approach is very effective, outperforms other existing methods and makes it possible to converge to almost all the images of the user's "class of interest" with a very small labeling effort. A demo is available through our image retrieval tool kit (IRTK).
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paper_title: Relevance feedback algorithm based on learning from labeled and unlabeled data

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Supervised learning algorithms (relevance feedback (RF) algorithms) are often used in content based image retrieval (CBIR) systems to enhance interactive search and browsing of image databases. One of the issues associated with RF based CBIR systems is the lack of a large training set. Labeling of images is a time consuming activity and user's usually do not have the patience to label a large set. The challenge is to somehow leverage the much larger set of unlabeled images to improve the performance of CBIR systems. In this paper we propose a novel RF algorithm which learns from both labeled and unlabeled data. Our proposed algorithm also uses active learning so as to maximize the information gained from a given amount of user feedback.
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paper_title: A similarity learning approach to content-based image retrieval: application to digital mammography

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In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.
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paper_title: A relevance feedback CBIR algorithm based on fuzzy sets

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CBIR (content-based image retrieval) systems attempt to allow users to perform searches in large picture repositories. In most existing CBIR systems, images are represented by vectors of low level features. Searches in these systems are usually based on distance measurements defined in terms of weighted combinations of the low level features. This paper presents a novel approach to combining features when using multi-image queries consisting of positive and negative selections. A fuzzy set is defined so that the degree of membership of each image in the repository to this fuzzy set is related to the user's interest in that image. Positive and negative selections are then used to determine the degree of membership of each picture to this set. The system attempts to capture the meaning of a selection by modifying a series of parameters at each iteration to imitate user behavior, becoming more selective as the search progresses. The algorithm has been evaluated against four other representative relevance feedback approaches. Both the performance and usability of the five CBIR systems have been studied. The algorithm presented is easy to use and yields the highest performance in terms of the average number of iterations required to find a specific image. However, it is computationally more expensive and requires more memory than two of the other techniques.
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paper_title: Association-based image retrieval

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With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content-based image retrieval (CBIR) systems have been developed in the last decade. A typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the database with similar features. Recent advances in CBIR systems include relevance feedback based interactive systems. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, we propose a new approach for image storage and retrieval called association-based image retrieval (ABIR). We try to mimic human memory. The human brain stores and retrieves images by association. We use a generalized bi-directional associative memory (GBAM) to store associations between feature vectors. The results of our simulation are presented in the paper.
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paper_title: SVM-based relevance feedback using random subspace method

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Relevance feedback (RF) schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM based RF is often poor when the number of labeled feedback samples is small. In order to solve this problem, we propose an RF algorithm using the random subspace method. The algorithm can overcome the classifier unstable and over-fitting problems that are common to the SVM based RF. Through extensive experiments on 17, 800 images, the proposed algorithm is shown to outperform existing algorithms significantly.
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paper_title: Feature space warping: an approach to relevance feedback

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Relevance feedback has been shown to be an effective tool to enhance content-based information retrieval (CBIR) systems. We propose a new approach to relevance feedback by warping the database's feature space, or shifting the objects' data points in a controlled manner responding to user feedback. We demonstrate that given consistent feedback, the performance of the retrieval system can be significantly increased.
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paper_title: A unified log-based relevance feedback scheme for image retrieval

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Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.
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paper_title: Content-based medical image retrieval (CBMIR): an intelligent retrieval system for handling multiple organs of interest

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A medical image usually contains images of several organs, with each organ being unique. It is a prerequisite for a physician to attentively examine more than one organ, so-called organs of interest. In this paper, we present an experimental design of an intelligent content-based medical image retrieval (CBMIR) for handling multiple organs of interest through three main processes. First, CBMIR identifies all such organs by comparing the images directly using the Hausdorff distance to the single, healthy organs stored in an organ database. After that, CBMIR builds image classes using neural networks and, finally, recognizes the proper class for a query image using a multicriteria optimization approach.
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paper_title: Relevance Feedback Technique for Content-Based Image Retrieval using Neural Network Learning

paper_content:

Relevance feedback (RF) is an interactive process in content-based image retrieval (CBIR), which refines the retrievals to a particular query by using user's feedback on previously retrieved results. In this paper, by changing the process of relevance feedback into a learning problem of neural network, a relevance feedback technique for content-based images retrieval by neural network learning (NELIR) is introduced, which can improve user interaction with image retrieval systems by fully exploiting similarity information. NELIR can describe the distribution of positive feedback sample images in feature space with a set of neighboring clusters produced through constructing neural network, for accurately reflecting their semantic relevance. In particular, constructing neural network is dynamic. The neural network depends on which images are retrieved in response to the query. On the other hand, NELIR is independent of the specific feature extraction and similarity measure. Thus, it may be embedded in many current CBIR systems to improve the performance of image retrieval. The performance of a prototype system using NELIR is evaluated on a database of 2,000 images. Experimental results demonstrate improved performance compared with a traditional CBIR system without NELIR algorithm using the same image similarity measure.
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paper_title: Bayesian Active Learning in Relevance Feedback for Image Retrieval

paper_content:

One of the fundamental problems in content-based image retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow the gap, relevance feedback (RF) is introduced into CBIR. However, most RF methods are challenged by small size sample collection and asymmetric sample distributions between the positive and the negative samples. In this paper, a Bayesian active learning (BAL) mechanism is proposed to overcome these problems. First, by defining the confidence, we design a new selection criterion for the images with the low confidence, which to be labeled by user, and then the learner is retrained by the most informative samples obtained from the last round feedback. Moreover, different learning strategies are used for estimating the distributions of the positive and the negative samples. Based on above methods, the retrieval performance can be enhanced. The experimental results on Corel image database demonstrate the effectiveness of the proposed algorithm.
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paper_title: Fuzzy relevance feedback in content-based image retrieval systems using radial basis function network

paper_content:

This paper presents a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems based on soft-decision. An efficient learning approach is proposed using a fuzzy radial basis function network (FRBFN). Conventional binary labeling schemes require a crisp decision to be made on the relevance of the retrieved images. However, user interpretation varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information priority. Therefore, fuzzy relevance feedback is introduced in this paper to integrate the users' fuzzy interpretation of visual content into the notion of relevance feedback. Based on the users' feedbacks, an FRBFN is constructed, and the underlying parameters and network structure are optimized using a gradient-descent training strategy. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.
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paper_title: Relvance Feed Back Content Based Image Retrieval using multiple features

paper_content:

With the emerge of multimedia, communication and processing, a typical image database has a large volume of data. It is an essential to build an efficient retrieval system to browse through the entire database. In this paper we proposed precise Relevance Feed Back (RFB) Content Based Image Retrieval (CBIR) using multiple features based on interactive retrieval approach which will extensively reduces the semantic gap between low-level features and high-level semantics. Relevance Feed Back improves the retrieval accuracy of Content Based Image Retrieval(CBIR) by modifying the query based on the user's feedback in which the user can select the most relevant images and provide a weight of preference for each relevant image. In our approach we extracted multiple features by integrating color, texture and shape features with Relevance Feed Back. We extracted color features by cumulative histogram, texture feature are exploited by using Color co-occurrence matrix (CCM) and we describe the shape information contained in an image on the basis of its significant edges. We used Euclidean distance as the similarity measure. In our experiment we used Corel real-world image databases with 1000 images, divided into 10 categories, each category containing 100 images including landscapes, animals, plants, monuments, transport (cars, planes). Our results reflects that integrating color, texture and shape for retrieving similar images improves the retrieval performance with respect to retrieval accuracy and recall rate as compared with the classical color feature. During the experiment we also observed that incorporating Relevance Feed Back (RFB) with multiple features outperforms in comparison with multifeature vector without Relevance Feed Back method.
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paper_title: A new CBIR system using sift combined with neural network and graph-based segmentation

paper_content:

In this paper, we introduce a new content-based image retrieval (CBIR) system using SIFT combined with neural network and Graph-based segmentation technique. Like most CBIR systems, our system performs three main tasks: extracting image features, training data and retrieving images. In the task of image features extracting, we used our new mean SIFT features after segmenting image into objects using a graph-based method. We trained our data using neural network technique. Before the training step, we clustered our data using both supervised and unsupervised methods. Finally, we used individual object-based and multi object-based methods to retrieve images. In the experiments, we have tested our system to a database of 4848 images of 5 different categories with 400 other images as test queries. In addition, we compared our system to LIRE demo application using the same test set.
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paper_title: Extraction of feature subspaces for content-based retrieval using relevance feedback

paper_content:

In the past few years, relevance feedback (RF) has been used as an effective solution for content-based image retrieval (CBIR). Although effective, the RF-CBIR framework does not address the issue of feature extraction for dimension reduction and noise reduction. In this paper, we propose a novel method for extracting features for the class of images represented by the positive images provided by subjective RF. Principal Component Analysis (PCA) is used to reduce both noise contained in the original image features and dimensionality of feature spaces. The method increases the retrieval speed and reduces the memory significantly without sacrificing the retrieval accuracy.
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paper_title: Experiment with reduced feature vector in CBIR system with relevance feedback

paper_content:

A CBIR system with relevance feedback is described. System uses feature vectors (FVs) with only 24 components describing color, line directions and texture. The reduction of FV dimension is based on the statistics of global image features. The proposed system was tested over Corel IK dataset and results are compared to some other CBIR systems with or without FV reduction.
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paper_title: Improved Image Retrieval Using Automatic Image Sorting and Semi-automatic Generation of Image Semantics

paper_content:

In this paper we propose a new image search system using keyword annotations and low-level visual meta-data to generate inter-image relationships. Unlike other approaches the new system does not try to learn the degree of confidence between images and associated keywords. We rather propose to model the degree of similarity between images by building up a network of linked images. The weights of the inter-image links are learned from the userspsila interaction with the system only. For each image search a set of candidate images is selected from a visually sorted arrangement of result images. This candidate set is used to refine the result by filtering out non-suiting images from a larger set of further result images. Semantic inter-image relation-ships of images can be modeled by collecting the candidate sets from many searches. Our system improves Internet image search significantly.
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paper_title: A new segmentation approach in structured self-organizing maps for image retrieval

paper_content:

Content-based image retrieval (CBIR) is the problem of searching for digital images in large databases. In order to search and index the images, different models of Self-organizing Maps (SOM), such as the Tree-Structured self labeled self-organizing maps (TS-SL-SOM), were used because combines features of data quantization and topology preservation with good results. TS-SL-SOM has a dynamic and data-driven tree topology with maps as nodes. From top to down, maps are segmented and each labeled region generates a new child map in the next level. For image retrieval, the feature vectors of images were mapped into the TS-SL-SOM neural network. Although it has presented good results when applied for indexing images, in comparison with other structures. In this paper a new SOM segmentation is proposed for improving the results in CBIR. The proposed algorithm is based in the well known idea that in the edges of segments the activation of neurons is less than in the center of segments. Better retrieval results were accomplished when using the proposed method in comparison with the traditional method.
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paper_title: Combining self-organizing neural nets with multivariate statistics for efficient color image retrieval

paper_content:

An efficient novel strategy for color-based image retrieval is introduced. It is a hybrid approach combining a data compression scheme based on self-organizing neural networks with a nonparametric statistical test for comparing vectorial distributions. First, the color content in each image is summarized by representative RGB-vectors extracted using the Neural-Gas network. The similarity between two images is then assessed as commonality between the corresponding representative color distributions and quantified using the multivariate Wald-Wolfowitz test. Experimental results drawn from the application to a diverse collection of color images show a significantly improved performance (approximately 10-15% higher) relative to both the popular, simplistic approach of color histogram and the sophisticated, computationally demanding technique of Earth Mover's Distance.
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paper_title: Using Relevance Feedback in Bridging Semantic Gaps in Content-Based Image Retrieval

paper_content:

Content-based image retrieval (CBIR) is a difficult area of research in multimedia systems. The research has proved extremely difficult because of the inherent problems in proper automated analysis and feature extraction of the image to facilitate proper classification of various objects. An image may contain more than one objects and to segment the image in line with object features to extract meaningful objects and then classify it in high-level like table, chair, car and so on has become a challenge to the researchers in the field. The latter part of the problem, the gap between low-level features like color, shape, texture, spatial relationships and high-level definitions of the images is called the semantic gap. Until we solve these problems in an effective way, the efficient processing and retrieval of information from images will be difficult to achieve. In this paper we explore the possibilities of how relevance feedback can help us solve this problem of semantic gap although lot of works have already been done using the concepts of relevance feedback in this area. This would enable efficient image retrieval for internet of the future.
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paper_title: Improving image retrieval performance with negative relevance feedback

paper_content:

Learning user perception of an image is a challenging issue in interactive content-based image retrieval (CBIR) systems. These systems employ relevance feedback mechanism to learn user perception in terms of a set of model-parameters and in turn iteratively improve the retrieval performance. Since the quantity of user feedback is expected to be small, learning the user's perception essentially involves parameter estimation with very few training points. We propose a novel, and more efficient method for relevance feedback. Contrary to existing geometric model-based relevance feedback methods, the proposed technique explicitly uses information about irrelevant data points to estimate the parameters of the model. This algorithm iteratively updates the parameters of the similarity metric so as to fit the relevant examples while excluding the irrelevant ones. This is achieved by modifying the weights associated with the relevant examples. Experiments on image and synthetic datasets demonstrate the retrieval effectiveness of the proposed approach.
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paper_title: Learning user preference in a personalized CBIR system

paper_content:

A new approach for learning user preference in a personalized content-based image retrieval (CBIR) system is proposed in this study. This approach provides users with textual descriptions, visual examples, and relevance feedbacks to find target images. The user query can be expressed by syntactic rules and semantic rules. To build a personalized CBIR system, two problems should be overcome in advance, including the semantic gap and the human perception subjectivity. In this study, the semantic gap is bridged through linguistic term sets, which are represented as fuzzy membership functions. The human perception subjectivity is modelled from relevance feedbacks through profile updating and feature re-weighting algorithms. The user preference is stored in a personal profile for further retrieval. Experimental results support the effectiveness of the proposed approach.
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paper_title: Strategy of combining random subspace and diversified active learning in CBIR

paper_content:

Generally speaking, several aspects related to relevance feedback based CBIR include what means should be adopted for approximate semantic description of image content, what strategies be applied to sample labeling in feedback and what relevance model would be built for online discrimination. Using random sampling strategy, we construct a set of random subspaces for learning multiple intrinsic descriptions of image content, with each of which stable component classifier can be trained. To enhance the generalization capability of relevance model, the diversified active learning is carried out by collecting more informative samples, i.e. those samples spreading around decision boundary dispersedly. The final favorable performance also contributes to the application of ensemble scheme on individual component classifier.
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paper_title: Medical image retrieval: Multiple regression models for user's search target

paper_content:

Breast cancer has been one of leading cancers in women around the world. A great number of digital mammograms are generated in hospitals and screening centers. Those digital mammograms can further be used for study and research by medical professionals. Content-based image retrieval refers to the retrieval of images whose contents are similar to a query example, using information derived from the images themselves. Relevance feedback, expressing the user's search target, can be used to bridge the semantic gap and improve the performance of CBIR systems. This study proposes a learning method for relevance feedback learning, which develops multiple logistic regression models to generalize the classification problem and provide an estimate of probability of class membership. To build the model, relevance feedback is utilized as the training data and the IRLS method is applied to estimate the parameters of the regression model and compute the maximum likelihood. Logistic regression models are created individually. After logistic regression models are fitted, discriminating features are selected by the measure of goodness of fit statistics. The weights of those discriminating features can be assigned according to their individual contributions to the maximum likelihood. The probability of the membership of the relevant class can therefore be obtained for each image of the database. Experimental results show that the proposed learning method can effectively improve the average precision from 30% to 65% through five iterations of relevance feedback rounds.
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paper_title: Concept Pre-digestion Method for Image Relevance Reinforcement Learning

paper_content:

Relevance feedback (RF) is commonly used to improve the performance of CBIR system by allowing incorporation of user feedback iteratively. Recently, a method called image relevance reinforcement learning (IRRL) has been proposed for integrating several existing RF techniques as well as for exploiting RF sessions of multiple users. The precision obtained at the end of every iteration is used was a reward signal in the Q-learning based reinforcement learning (RL) approach. The objective of learning in IRRL is to estimate the optimal RF technique to be applied for a given query at a specific iteration. The main drawback of IRRL is its prohibitive learning time and storage requirement. We propose a way of addressing these difficulties by performing `pre-digestion' of concepts before applying IRRL. Experimental results on two databases of images demonstrated the viability of the proposed approach
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paper_title: Modelling agents in C++CL for content-based image retrieval

paper_content:

We describe a hybrid system for dealing with content-based retrieval from a library of images. An agent architecture is used in conjunction with a neural network to match and retrieve images. We explain how we define and use our image indices which allows us to treat the problem in a distributed fashion. We combine the indices with a multiple agent approach to help reduce our search space and to retrieve an image as a parallel action. The resulting system is one which is flexible but controlled. A neural network is used to generate a feature vector from the images which is used by the agents. The agents are based on the theories behind Lilies. Lilies (Localisation and InterLeaving strategies) was developed to deal with multiple agents for a planning environment. Lilies was used to prototype the initial representation of the agents used for searching the database. Although an implementation of the search-agents has been developed in Lilies, we also describe in this paper the design to convert the agent model to C++CL platform. C++CL is an object oriented framework layered on top of PVM which allows planned and unplanned reconfiguration of distributed systems. We introduce two issues of distributed systems: a neural net, for image compression; and the design of an agent searching architecture using C++CL.
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paper_title: Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns

paper_content:

Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback (NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.
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paper_title: Semantics-Based Art Image Retrieval Using Linguistic Variable

paper_content:

More and more digitized art images are accumulated and expanded in our daily life and techniques need to be established on how to organize and retrieval them. Though content-based image retrieval (CBIR) got great progress, current low-level visual information based retrieval technology does not allow users to retrieval images by high-level semantics. We propose an approach to describe and to extract the fuzzy aesthetic semantics of art images. Accordance with the subjectivity and vagueness of human aesthetic perception, we utilize the linguistic variable to describe the image aesthetic semantics, so it becomes possible to depict the image in linguistic expression such as 'very action'. Furthermore, we apply the feedforward neural network to model the process of human aesthetic perception and to extract the fuzzy semantic feature vector. The retrieval methodology makes users more naturally find desired images by linguistic expression and experimental implementation demonstrates good potential on retrieval art images with a human-accustomed manner.
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paper_title: Relevance feedback of content-based image retrieval using support vector machine

paper_content:

The rapid growth of computer technologies and the advent of the World Wide Web have increased the amount and the complexity of multimedia information. A content-based image retrieval (CBIR) system has been developed as an efficient image retrieval tool, whereby the user can provide their query to the system to allow it to retrieve the user's desired image from the image database. However, the traditional relevance feedback of CBIR has some limitations that will decrease the performance of the CBIR system, such as the imbalance of training-set problem, classification problem, limited information from user problem, and insufficient training-set problem. Therefore, in this study, we proposed an enhanced relevance-feedback method to support the user query based on the representative image selection and weight ranking of the images retrieved. The support vector machine (SVM) has been used to support the learning process to reduce the semantic gap between the user and the CBIR system. From these experiments, the proposed learning method has enabled users to improve their search results based on the performance of CBIR system. In addition, the experiments also proved that by solving the imbalance training set issue, the performance of CBIR could be improved.
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paper_title: Analysis of Relevance Feedback in Content Based Image Retrieval

paper_content:

Relevance feedback in content based image retrieval (CBIR) has been an active field of research for quite some time now. Many schemes and techniques of relevance feedback exist with many assumptions and operating criteria. Yet there exist few ways of quantitatively measuring and comparing different relevance feedback algorithms. Such analysis is necessary if a CBIR system is to perform consistently. In this paper we propose an abstract model of a CBIR system where the effects of different modules over the entire system is observed. Using this model we thoroughly analyse performance a set of basic relevance feedback algorithms. Besides using standard measures like precision and recall we also suggest two new measures to gauge the performance of any contemporary CBIR system
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paper_title: Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

paper_content:

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance
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paper_title: Online learning of relevance feedback from expert readers for mammogram retrieval

paper_content:

In content-based image retrieval (CBIR) relevance feedback schemes have been studied as a means to boost the retrieval performance in recent years. Despite the efforts in development of efficient algorithms for retrieving desired images from image databases, there often remains a gap between low-level image features and high-level semantic understanding in CBIR systems. In this paper, we investigate a technique based on online learning by relevance feedback for retrieval of mammogram images that contain perceptually similar lesions with clustered microcalcifications. Our approach applies support vector machine (SVM) regression for supervised learning and employs the concept of incremental learning to incorporate relevance feedback online. The proposed approach is demonstrated using a database of 200 mammogram images with clustered microcalcifications scored by experienced radiologists.
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paper_title: Graph cuts in content-based image classification and retrieval with relevance feedback

paper_content:

Content-based image retrieval (CBIR) has suffered from the lack of linkage between low-level features and high-level semantics. Although relevance feedback (RF) CBIR provides a promising solution involving human interaction, certain query images poorly represented by low-level features still have unsatisfactory retrieval results. An innovative method has been proposed to increase the percentage of relevance of target image database by using graph cuts theory with the maximum-flow/minimum-cut algorithm and relevance feedback. As a result, the database is reformed by keeping relevant images while discarding irrelevant images. The relevance is increased and thus during following RF-CBIR process, previously poorly represented relevant images have higher probability to appear for selection. Better performance and retrieval results can thus be achieved.
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paper_title: Relevance feedback for content-based image retrieval using the Choquet integral

paper_content:

Relevance feedback is a technique to learn the user's subjective perception of similarity between images, and has recently gained attention in content based image retrieval (CBIR). Most relevance feedback methods assume that the individual features that are used in similarity judgments do not interact with each other. However, this assumption severely limits the types of similarity judgments that can be modeled. The authors explore a more sophisticated model for similarity judgments based on fuzzy measures and the Choquet integral, and propose a suitable algorithm for relevance feedback. Experimental results show that the proposed method is preferable to traditional weighted-average techniques. The proposed algorithm is being incorporated into a CBIR system developed at Korea Telecom.
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paper_title: Using Bayesian classifier in relevant feedback of image retrieval

paper_content:

Relevance feedback is a powerful technique in content-based image retrieval (CBIR) and has been an active research area for the past few years. In this paper, we propose a new relevance feedback approach based on a Bayesian classifier, and it treats positive and negative feedback examples with different strategies. For positive examples, a Bayesian classifier is used to determine the distribution of the query space. A 'dibbling' process is applied to penalize images that are near the negative examples in the query and retrieval refinement process. The proposed algorithm also has a progressive learning capability that utilizes past feedback information to help the current query. Experimental results show that our algorithm is effective.
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paper_title: A graphic-theoretic model for incremental relevance feedback in image retrieval

paper_content:

Many traditional relevance feedback approaches for content-based image retrieval (CBIR) can only achieve limited short-term performance improvement without benefiting long-term performance. To remedy this limitation, we propose a graphic-theoretic model for incremental relevance feedback in image retrieval. Firstly, a two-layered graph model is introduced that describes the correlations between images. A teaming strategy is then suggested to enrich the graph model with semantic correlations between images derived from user feedback. Based on the graph model, we propose a link analysis approach for image retrieval and relevance feedback. Experiments conducted on real-world images have demonstrated the advantage of our approach over traditional approaches in both short-term and long-term performance.
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paper_title: Active sample-selecting and manifold learning-based relevance feedback method for synthetic aperture radar image retrieval

paper_content:

Content-based image retrieval (CBIR) provides an effective way to address the increasing need for intelligent data access to synthetic aperture radar (SAR) image repositories. In CBIR, a critical component is relevance feedback (RF), which is used to bridge the ‘semantic gap’. This study proposes a new RF method with active sample-selecting and manifold learning for CBIR of SAR images. In this method, the authors adopt a modified maximum margin projection with a new neighbourhood estimation criterion to discover both the geometrical structure and discriminant structure of the underlying data manifold. In order to achieve a satisfactory performance with limited feedback samples, the authors also propose an active sample selection strategy with which the diversity of feedback samples can be increased while the redundancy is decreased. The authors test our method on a TerraSAR-X image database and compare it with four other state-of-the-art RF methods. The superiority and validity of the authors’ method is proved by the retrieval results and the computing cost is acceptable for image retrieval applications.
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paper_title: Incremental hybrid Bayesian network in content-based image retrieval

paper_content:

A learning system is one of the future directions of the evolution of content-based image retrieval (CBIR) system. Relevance feedback (RF) is a technique that enables systems to learn from users. In the past few years, this technique has been used as an effective solution for content-based image retrieval. Based on information theory, this paper proposes an incremental hybrid Bayesian network approach -IHBN, which processes the examples with decision tree. Only for those examples that cannot be processed are resorted by Bayesian network. The model constructed by IHBN can extend easily. Its size and structure change dynamically while learning. Its distinct incremental learning mechanism can not only make inductive learning possible while lacking of domain knowledge, but also depress the noise sensibility of the learning algorithm. This approach retains the interpretability of Bayesian network and decision tree, while resulting in classifiers that outperform both constituents. We propose this approach in content-based image retrieval system with relevance feedback. By comparing the performance of our approach with most conventional approaches in CBIR with relevance feedback, the experimental results demonstrate that the IHBN approach is more feasible and effective in the content-based image retrieval
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paper_title: An image retrieval system with automatic query modification

paper_content:

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.
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paper_title: Learning similarity matching in multimedia content-based retrieval

paper_content:

Many multimedia content-based retrieval systems allow query formulation with the user setting the relative importance of features (e.g., color, texture, shape, etc.) to mimic the user's perception of similarity. However, the systems do not modify their similarity matching functions, which are defined during the system development. We present a neural network-based learning algorithm for adapting the similarity matching function toward the user's query preference based on his/her relevance feedback. The relevance feedback is given as ranking errors (misranks) between the retrieved and desired lists of multimedia objects. The algorithm is demonstrated for facial image retrieval using the NIST Mugshot Identification Database with encouraging results.
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paper_title: Fuzzy relevance feedback in content-based image retrieval

paper_content:

In this paper, a new notion called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme requires a crisp decision to be made on the relevance of the retrieved images. However, user interpretation varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information request. It is, therefore, inadequate to describe user's fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgment for relevance ranking. A radial basis function (RBF) network with local modeling structure is used for similarity learning. Experimental results show that our system is more user-adaptive, and it can achieve better performance compared with other conventional retrieval systems which are based on hard-decision and global modeling.
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paper_title: Back-propagation algorithm for relevance feedback in image retrieval

paper_content:

Content-based image retrieval (CBIR) usually relies on pre-attentive similarities. Results are often coarse because of the gap between the pre-attentive level and the semantic level of the user's request. The aim of relevance feedback is to refine results by taking user's expertise into account. This paper presents a new feedback architecture for CBIR. Images are compared through a weighted dissimilarity function which can be represented as a "network of dissimilarities". The weights are updated via an error backpropagation algorithm using the user's annotations of the successive set of result images. It allows an iterative refinement of the search through a simple interactive process (the user has just to specify if images are relevant or not). A quality assessment realized with three databases containing about 10,000 images shows the performance improvement after feedback.
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paper_title: Retrieval with knowledge-driven kernel design: an approach to improving SVM-based CBIR with relevance feedback

paper_content:

The performance of SVM-based image retrieval is often constrained by the scarcity of training samples. The total number of image samples labeled by users in a retrieval session is very limited, and this small number of labeled samples cannot effectively represent the true distributions of positive and negative image classes, especially for the negative image class. This paper proposes a novel approach to deal with this problem. Instead of treating it as a problem, the mere existence of the small number of labeled images and their desired distribution in the kernel space is considered as prior knowledge from image retrieval to aid the design of the kernel used by SVMs. This is achieved by maximizing a criterion, such as one based on scatter matrices, through gradient-based search methods, incurring very little computational overhead to real-time retrieval process. Experimental results on two benchmark image databases demonstrate the improved retrieval performance by the dynamically designed kernel and hence the effectiveness of the proposed approach for SVM based image retrieval
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paper_title: Proto-reduct Fusion Based Relevance Feedback in CBIR

paper_content:

This paper proposes two related RF methods for use in CBIR. These two methods are based on a general classificatory analysis based framework for RF in CBMR that considers RF independently from retrieval. The proposed methods show how the user's information need expressed as a set of "proto-reducts'' can be used as the basis of a re-weighting technique that can improve subsequent retrieval The performance of the proposed methods is studied on two image collections with different characteristics and compared against an existing RF method.
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paper_title: Learning Pathological Characteristics from User's Relevance Feedback for Content-Based Mammogram Retrieval

paper_content:

Content-based image retrieval (CBIR) has been proposed to address the problem of image retrieval from medical image databases. Relevance feedback, explaining the user?s query concept, can be used to bridge the semantic gap and improve the performance of CBIR systems. This paper proposes a learning method for relevance feedback, which utilizes probabilistic model to generalize the 2-class problem and provide an estimate of probability of class membership. To build the probabilistic model, support vector machine (SVM) is applied to classify the mammograms, and then scale them to the probability of class membership. Experimental results show that the proposed learning method can effectively improve the average precision rate from 40% to 62% through five iterations of relevance feedback rounds.
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paper_title: Cubic-splines neural network- based system for Image Retrieval

paper_content:

Research in Content-Based Image Retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named SNNIR (Splines Neural Network-based Image Retrieval). SNNIR system makes use of a rapid and precise neural model. This model employs a cubic-splines activation function. By using the spline neural model, the gap between the low-level visual features and the high-level concepts is minimized. Experimental results show that the proposed system achieves high accuracy and effectiveness in terms of precision and recall compared with other CBIR systems.
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paper_title: L-gem based co-training for CBIR with relevance feedback

paper_content:

Relevance feedback has been developed for several years and becomes an effective method for capturing userpsilas concepts to improve the performance of content-based image retrieval (CBIR). In contrast to fully labeled training dataset in supervised learning, semi-supervised learning and active learning deal with training dataset with only a small portion of labeled samples. This is more realistic because one could easily find thousands of unlabeled images from the Internet. How to make use of such unlabeled resources on the Internet is an important research topic. Co-training method is to expand the number of labeled samples in semi-supervised learning by swapping training samples between two classifiers. In this work, we propose to apply the localized generalization error model (L-GEM) to co-training. Two radial basis function neural networks (RBFNN) with different features split is adopted in the co-training and the unlabeled samples with lowest L-GEM value is added to the co-training in next iteration. In the CBIR system, we output those positive images with lowest L-GEM value as the highest confident image and output those images with highest L-GEM to ask for user labeling. Higher the L-GEM value of a sample is, the less confident is the classifier to predict its image class. Experimental results show that the proposed method could effectively improve the image retrieval results.
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paper_title: Reduct-Based Result Set Fusion for Relevance Feedback in CBIR

paper_content:

Relevance feedback (RF) is a widely used technique to deal with the issues of user subjectivity and the semantic gap in content-based image retrieval (CBIR). We build on existing work that outlined a rough set based general framework called CAFe for RF and proposed a re-weighting strategy based on a rough set theoretic analysis of the user feedback. This paper presents a method that uses the approximation of the information need distilled from the user classification as the basis for multiple distinct retrievals. The final result set that is presented as the subsequent iteration to the user is obtained by fusing the result sets from the different retrievals. The method is demonstrated in the context of a simple test image collection for clarity. An analysis of the sample iterations of feedback is presented. The method presented remains independent of the retriever, relies on a conceptually appealing model of the user feedback and serves to establish the utility of the general framework.
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paper_title: WillHunter: interactive image retrieval with multilevel relevance

paper_content:

Relevance feedback has become a key component in CBIR system. Although most current relevance feedback approaches are based on dichotomous relevance measurement, this coarse measurement is a distortion of the reality. We study relevance feedback with multi-level relevance measurement to better identify the u ser needs and preferences. To validate the use of multi-level relevance measurement and our relevance feedback algorithm, we developed a CBIR prototype system - WillHunter. There are two novelties in our system, one is our SVM-based fast learning algorithm; another is the easy-to-use graphical user interface, especially the relevance-measuring instrument. Not only experiments are conducted to assess the algorithm, but also usability study is carried out to evaluate the user interface.
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paper_title: Learning region weighting from relevance feedback in image retrieval

paper_content:

The region-based approach to image retrieval has emerged as one of the most active research directions in the past few years. There are two crucial problems in the region-based systems: the weighting of regions and the use of relevance feedback. The former plays an important role in computing the region-based similarity of two images, while the latter can improve the efficiency and effectiveness of any CBIR system, if properly employed. In this paper, we propose Key-Region, a novel region weighting scheme that is based on the user's relevance feedback information. The region weight that coincides with human perception can not only be used in a query session, but also be memorized and accumulated for future queries. Experimental results on a database of about 10,000 general-purposed images show the effectiveness of our weighting scheme.
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paper_title: Image retrieval with feature selection and relevance feedback

paper_content:

This paper proposes a new content based image retrieval (CBIR) system combined with relevance feedback and the online feature selection procedures. A measure of inconsistency from relevance feedback is explicitly used as a new semantic criterion to guide the feature selection. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy. Experimental results show that the proposed method obtains higher retrieval accuracy than a commonly used approach.
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Title: Intelligent Image Retrieval Techniques: A Survey
Section 1: Introduction
Description 1: Discuss the historical background and rationale behind the development of intelligent image retrieval techniques.

Section 2: Content-based image retrieval using neural networks
Description 2: Present various neural network-based approaches for content-based image retrieval, highlighting their methodologies and performance outcomes.

Section 3: Relevance feedback techniques in image retrieval
Description 3: Describe different relevance feedback methods and their contributions to improving image retrieval accuracy.

Section 4: Support vector machines and machine learning in image retrieval
Description 4: Explore the integration of support vector machines and other machine learning techniques in image retrieval systems and their benefits.

Section 5: User interface and interaction in image retrieval systems
Description 5: Examine the role of interactive user interfaces and user preferences in enhancing the performance of image retrieval systems.

Section 6: Specialized image retrieval techniques
Description 6: Discuss image retrieval approaches tailored for specific types of images such as medical, 3D models, and geospatial images.

Section 7: Comparison of different intelligent image retrieval techniques
Description 7: Provide a comparative analysis of various intelligent image retrieval methods, including neural networks, relevance feedback, and machine learning-based approaches.

Section 8: Conclusion
Description 8: Summarize the discussed techniques and their impact on the field of image retrieval, and suggest potential future research directions.