A Literature Review of Image Retrieval based on Semantic Concept
---
paper_title: Learning to reduce the semantic gap in web image retrieval and annotation

paper_content:

We study in this paper the problem of bridging the semantic gap between low-level image features and high-level semantic concepts, which is the key hindrance in content-based image retrieval. Piloted by the rich textual information of Web images, the proposed framework tries to learn a new distance measure in the visual space, which can be used to retrieve more semantically relevant images for any unseen query image. The framework differentiates with traditional distance metric learning methods in the following ways. 1) A ranking-based distance metric learning method is proposed for image retrieval problem, by optimizing the leave-one-out retrieval performance on the training data. 2) To be scalable, millions of images together with rich textual information have been crawled from the Web to learn the similarity measure, and the learning framework particularly considers the indexing problem to ensure the retrieval efficiency. 3) To alleviate the noises in the unbalanced labels of images and fully utilize the textual information, a Latent Dirichlet Allocation based topic-level text model is introduced to define pairwise semantic similarity between any two images. The learnt distance measure can be directly applied to applications such as content-based image retrieval and search-based image annotation. Experimental results on the two applications in a two million Web image database show both the effectiveness and efficiency of the proposed framework.
---
paper_title: Image Retrieval from the World Wide Web: Issues, Techniques, and Systems

paper_content:

With the explosive growth of the World Wide Web, the public is gaining access to massive amounts of information. However, locating needed and relevant information remains a difficult task, whether the information is textual or visual. Text search engines have existed for some years now and have achieved a certain degree of success. However, despite the large number of images available on the Web, image search engines are still rare. In this article, we show that in order to allow people to profit from all this visual information, there is a need to develop tools that help them to locate the needed images with good precision in a reasonable time, and that such tools are useful for many applications and purposes. The article surveys the main characteristics of the existing systems most often cited in the literature, such as ImageRover, WebSeek, Diogenes, and Atlas WISE. It then examines the various issues related to the design and implementation of a Web image search engine, such as data gathering and digestion, indexing, query specification, retrieval and similarity, Web coverage, and performance evaluation. A general discussion is given for each of these issues, with examples of the ways they are addressed by existing engines, and 130 related references are given. Some concluding remarks and directions for future research are also presented.
---
paper_title: Image Retrieval in Multimedia Databases: A Survey

paper_content:

In the past decades the advancement in the area of database management systems shifts towards multimedia. Multimedia information is very expressive, self explanatory, narrative, etc. Now a day the development of digital media, advanced network infrastructure and the easily available consumer electronics makes the multimedia revolution to run in an alarming rate [4] Inline with the advancement of database technology that incorporates multimedia data, an open question that always rose in the technology is how to retrieve/search images in the multimedia databases. There are a huge number of research works focusing on the searching mechanisms in image databases for efficient retrieval and tried to give supplementary suggestions on the overall systems. The growing of digital medias (digital camera, digital video, digital TV, e-book, cell phones, etc.) gave rise to the revolution of very large multimedia databases, in which the need of efficient storage, organization and retrieval of multimedia contents came into question. Among the multimedia data, this survey paper focuses on the different methods (approaches) and their evaluation techniques used by many of recent research works on image retrieval system. Many researchers develop and use lots of approaches towards image retrieval. This paper, in general, classified image retrieval into text based and content based, including the newly growing ontology based image retrieval system as one focus. We address, in this paper, the challenges, techniques and evaluation methods used in image retrieval systems through a detailed look of most recent works
---
paper_title: A survey of content-based image retrieval with high-level semantics

paper_content:

In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the 'semantic gap' between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the 'semantic gap': (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users' intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
---
paper_title: Search Behaviours of Image Users: A Pilot Study on Museum Objects

paper_content:

Many factors can overwhelm the image searcher who is trying to retrieve images. Consequently, retrieving images is still a problem for the majority of individuals, especially for images associated with text written in different languages, unknown to users. The purpose of this exploratory study is to investigate the factors affecting search behaviours of image users and to examine how individuals formulate their queries to retrieve museum object images indexed in different languages, using different search engines. This study also compared the search behaviours using different types of search engines to retrieve specific museum object images. It also highlighted how multilingual search functionalities are perceived and used when performing an image search task in a multilingual retrieval context. Thirty participants randomly divided into three independent groups assigned to one different search engine was used for this study. Each participant was asked to retrieve three mages using an all-purpose search engine and a specialized search engine. Once the retrieval of the images was completed, the participants filled out a questionnaire to gather comments on the retrieval tasks they performed and information on their search behaviours of Web images indexed in different languages. Multilingualism plays a strategic role in the quality and effectiveness of communication services offered on the Internet. Consequently, it is of significant importance to make information available to the largest audience possible and to overcome language barriers by providing tools suited to the real and current needs of image searchers. ::: The main contribution of this pilot study is to enhance the knowledge and understanding of image searching behaviour, in order to provide a basis for the modelling of a new search interface that takes into account the needs and expectations of real users.
---
paper_title: Web Image Search by Automatic Image Annotation and Translation

paper_content:

There has been a growing interest in implementing online Web image search engine in the semantic level. However, most existing practical systems including popular commercial Web image search engines like Google and Yahoo! are either text- based or a simple hybrid of texts and visual features. This paper proposes a novel Web Image Search by Automatic Image Annotation and Translation (WISAIAT) system by using automatic image annotation and translation. We develop a technology which learns semantic image concepts from image contents and translates unstructured images into textual documents, so that images are indexed and retrieved in the same way as textual documents. Existing database management systems can be used to effectively manage image contents and image search can act as efficient as text search by translating images to textual documents through large scale machine learning. Experiments in both the Corel dataset and real Web dataset are performed to validate our system and the results are promising. This system suggests a new combination of texts and visual features to achieve a semantic Web image search and expected to become a reranking system to the existing Web image search result available online via the Internet.
---
paper_title: Improving Image Retrieval Using Semantic Resources

paper_content:

Many people use the Internet to find pictures of things. When extraneous images appear in response to simple queries on a search engine, the user has a hard time understanding why his seemingly clear request was not properly satisfied. If the computer could only understand what he wanted better, then maybe the results would be more precise. The introduction of an ontology, though hidden from the user, into current image retrieval engines may provide more accurate image responses to his query. The improvement of the results translates into the possibility of offering structured results, to disambiguate queries and to provide more interactivity options to the user, transforming the current string of character based retrieval into a concept based process. Each one of these aspects is presented and examples are used to support our proposals. We equally discuss the notion of picturability and justify our choice to work exclusively with entities that can be directly represented in a picture. Coordinating the use of a lexical ontology (an OWL representation of WordNet) with image processing techniques, we have developed a system that, given an initial query, automatically returns images associated with the query using automatic reformulation (each concepts is represented by its deepest hyponyms from the ontology). We show that picking randomly from this new set of pictures provides an improved representation for the initial, more general query. We also treat the visual aspects of the images for these deepest hyponyms (the leaves of WordNet). The depictions associated to leaf categories are clustered into coherent sets using low-level image features like color and texture. Some limitations (e.g. the quality and coverage of the semantic structure, the impossibility to answer complex queries) of the ontology based retrieval are equally discussed.
---
paper_title: Unsupervised Learning of Visual Sense Models for Polysemous Words

paper_content:

Polysemy is a problem for methods that exploit image search engines to build object category models. Existing unsupervised approaches do not take word sense into consideration. We propose a new method that uses a dictionary to learn models of visual word sense from a large collection of unlabeled web data. The use of LDA to discover a latent sense space makes the model robust despite the very limited nature of dictionary definitions. The definitions are used to learn a distribution in the latent space that best represents a sense. The algorithm then uses the text surrounding image links to retrieve images with high probability of a particular dictionary sense. An object classifier is trained on the resulting sense-specific images. We evaluate our method on a dataset obtained by searching the web for polysemous words. Category classification experiments show that our dictionary-based approach outperforms baseline methods.
---
paper_title: Ontology-Based Semantic Web Image Retrieval by Utilizing Textual and Visual Annotations

paper_content:

The goal of traditional visual or textual-based image retrieval is to satisfy user’s queries by associating the images and semantic concepts effectively. As a result, perceptual structures of images have attracted researchers’ attention in recent studies. However, few past studies have been made on achieving semantic image retrieval by using image annotation techniques. To catch user’s ontological intention, we propose a new approach, namely Intelligent Web Image FetchER (iWIFER), which simultaneously considers the ontological requirements in usability, intelligence and effectiveness. Based on the proposed visual and textual-based annotation models, the image query becomes easy and effective. Through empirical evaluations, our annotation models can deliver accurate results for semantic web image retrieval.
---
paper_title: An Efficient Local Invariant Region Detector for Image Retrieval

paper_content:

An efficient algorithm for detecting invariant and salient image regions is presented. The detected regions are highly repeatable under various viewpoint and illumination variations. With the proposed implementation technique, the region detector becomes much more efficient and practical. Coupled with the distinctiveness and robustness of the detector, fast indexing and retrieval become possible.
---
paper_title: Web Image Search by Automatic Image Annotation and Translation

paper_content:

There has been a growing interest in implementing online Web image search engine in the semantic level. However, most existing practical systems including popular commercial Web image search engines like Google and Yahoo! are either text- based or a simple hybrid of texts and visual features. This paper proposes a novel Web Image Search by Automatic Image Annotation and Translation (WISAIAT) system by using automatic image annotation and translation. We develop a technology which learns semantic image concepts from image contents and translates unstructured images into textual documents, so that images are indexed and retrieved in the same way as textual documents. Existing database management systems can be used to effectively manage image contents and image search can act as efficient as text search by translating images to textual documents through large scale machine learning. Experiments in both the Corel dataset and real Web dataset are performed to validate our system and the results are promising. This system suggests a new combination of texts and visual features to achieve a semantic Web image search and expected to become a reranking system to the existing Web image search result available online via the Internet.
---
paper_title: Automatic Web Image Annotation via Web-Scale Image Semantic Space Learning

paper_content:

The correlation between keywords has been exploited to improve Automatic Image Annotation(AIA). Differing from the traditional lexicon or training data based keyword correlation estimation, we propose using Web-scale image semantic space learning to explore the keyword correlation for automatic Web image annotation. Specifically, we use the Social Media Web site: Flickr as Web scale image semantic space to determine the annotation keyword correlation graph to smooth the annotation probability estimation. To further improve Web image annotation performance, we present a novel constraint piecewise penalty weighted regression model to estimate the semantics of the Web image from the corresponding associated text. We integrate the proposed approaches into our Web image annotation framework and conduct experiments on a real Web image data set. The experimental results show that both of our approaches can improve the annotation performance significantly.
---
paper_title: Using Multi-Modal Semantic Association Rules to fuse keywords and visual features automatically for Web image retrieval

paper_content:

A recent trend for image search is to fuse the two basic modalities of Web images, i.e., textual features (usually represented by keywords) and visual features for retrieval. The key issue is how to associate the two modalities for fusion. In this paper, a new approach based on Multi-Modal Semantic Association Rule (MMSAR) is proposed to fuse keywords and visual features automatically for Web image retrieval. A MMSAR contains a single keyword and several visual feature clusters, which crosses and associates the two modalities of Web images. A customized frequent itemsets mining algorithm is designed for the particular MMSARs based on the existing inverted file, and a new support-confidence framework is defined for the mining algorithm. Based on the mined MMSARs, the keywords and the visual features are fused automatically in the retrieval process. The proposed approach not only remarkably improves the retrieval precision, but also has fast response time. The experiments are carried out in a Web image retrieval system, VAST (VisuAl & SemanTic image search), and the results show the superiority and effectiveness of the proposed approach.
---
paper_title: Automatic Semantic Annotation of Real-World Web Images

paper_content:

As the number of Web images is increasing at a rapid rate, searching them semantically presents a significant challenge. Many raw images are constantly uploaded with little meaningful direct annotations of semantic content, limiting their search and discovery. In this paper, we present a semantic annotation technique based on the use of image parametric dimensions and metadata. Using decision trees and rule induction, we develop a rule-based approach to formulate explicit annotations for images fully automatically, so that by the use of our method, semantic query such as "sunset by the sea in autumn in New York" can be answered and indexed purely by machine. Our system is evaluated quantitatively using more than 100,000 Web images. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good recall and precision rates of sometimes over 80%. This approach enables a new degree of semantic richness to be automatically associated with images which previously can only be performed manually.
---
paper_title: Mining Cross-Modal Association Rules for Web Image Retrieval

paper_content:

To alleviate the known semantic gap, it is necessary to integrate the two-modal parts of Web images, i.e. the low-level visual features and high-level semantic concepts (which are usually represented by keywords), for Web image retrieval. In this paper, we associate the keyword and visual features of Web images from a different prospective and a new approach based on the cross-modal association rules is proposed to automatically integrate the keyword and visual features for Web image retrieval. A customized mining process is developed for the special association rule that crosses the two modals of Web images. The cross-modal association rule effectively associates the keyword and visual feature clusters, and seamlessly integrates the two modals of Web images in retrieval process. The proposed approach is utilized successfully in a Web image retrieval system named VAST (VisuAl & SemanTic image search).
---
paper_title: Improving image annotation via representative feature vector selection

paper_content:

How to bridge the semantic gap is currently a major research problem in Content-Based Image Retrieval (CBIR). Most applications are based on supervised machine-learning classifiers to match images with their related categories. Noisy training information has resulted in current systems having low accuracy, especially when using large numbers of vocabulary categories. In this paper, we describe the use of the Information Gain (IG) and AdaBoost learning algorithms for noise and outlier information filtering in the system training stage, thus improving the performance of image classification. Our experiments look at different numbers of target categories and image segmentation schemes.
---
paper_title: Constructing visual phrases for effective and efficient object-based image retrieval

paper_content:

The explosion of multimedia data necessitates effective and efficient ways for us to get access to our desired ones. In this article, we draw an analogy between image retrieval and text retrieval and propose a visual phrase-based approach to retrieve images containing desired objects (object-based image retrieval). The visual phrase is defined as a pair of frequently co-occurred adjacent local image patches and is constructed using data mining. We design methods on how to construct visual phrase and how to index/search images based on visual phrase. We demonstrate experiments to show our visual phrase-based approach can be very efficient and more effective than current visual word-based approach.
---
paper_title: Multi modal semantic indexing for image retrieval

paper_content:

Popular image retrieval schemes generally rely only on a single mode, (either low level visual features or embedded text) for searching in multimedia databases. Many popular image collections (eg. those emerging over Internet) have associated tags, often for human consumption. A natural extension is to combine information from multiple modes for enhancing effectiveness in retrieval. In this paper, we propose two techniques: Multi-modal Latent Semantic Indexing (MMLSI) and Multi-Modal Probabilistic Latent Semantic Analysis (MMpLSA). These methods are obtained by directly extending their traditional single mode counter parts. Both these methods incorporate visual features and tags by generating simultaneous semantic contexts. The experimental results demonstrate an improved accuracy over other single and multi-modal methods.
---
paper_title: Region-Based Semi-automatic Annotation Using the Bag of Words Representation of the Keywords

paper_content:

Automatic Image Annotation (AIA) tries to minimize the manual effort for image annotation. However, the performance of the AIA approaches is not satisfactory. The interaction of user is needed to solve this problem. The annotation is refined during the interaction by using semantic-based relevance feedback. This approach has a limit as only the annotations of found images during the interaction are updated. In this paper we introduce a novel method of semi-automatic annotation. The method is using visual feature representations of keywords which are improved during the region-based relevance feedback. The experiments show that this method gives good results and can be used to update the annotations for all images.
---
paper_title: A Semantic Retrieval Approach by Color and Spatial Location of Image Regions

paper_content:

The traditional content-based image retrieval system can not meet the users’ needs due to the ‘semantic gap’ between low-level features and the richness of human semantics. In an attempt to narrow the ‘semantic gap’, an approach which combines region-based image retrieval (RBIR) and relevance feedback (RF) with semantic color and spatial location features is presented in this paper. This method segments images into homogeneous regions and gives name for each region. In addition, the importance of each region is also taken into account. The Earth Mover’s Distance (EMD) is employed to measure the image similarity. To further improve the efficiency and accuracy of retrieval system, relevance feedback techniques are introduced in this paper. Experimental results on 1000 images database demonstrate the effectiveness and the efficiency of our approach is encouraging.
---
paper_title: A relevance feedback method based on genetic programming for classification of remote sensing images

paper_content:

This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions of interest (and those which are not). This feedback information is employed by a genetic programming approach to learning user preferences and combining image region descriptors that encode spectral and texture properties. Experiments demonstrate that the proposed method is effective for image classification tasks and outperforms the traditional MaxVer method.
---
paper_title: Relevance feedback in image retrieval: A comprehensive review

paper_content:

We analyze the nature of the relevance feedback problem in a continuous representation space in the context of content-based image retrieval. Emphasis is put on exploring the uniqueness of the problem and comparing the assumptions, implementations, and merits of various solutions in the literature. An attempt is made to compile a list of critical issues to consider when designing a relevance feedback algorithm. With a comprehensive review as the main portion, this paper also offers some novel solutions and perspectives throughout the discussion.
---
paper_title: A unified framework for semantics and feature based relevance feedback in image retrieval systems

paper_content:

The relevance feedback approach to image retrieval is a powerful technique and has been an active research direction for the past few years. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform optimization on multi-level image content model have been formulated. However, these methods only perform relevance feedback on the low-level image features and fail to address the images' semantic content. In this paper, we propose a relevance feedback technique, iFind, to take advantage of the semantic contents of the images in addition to the low-level features. By forming a semantic network on top of the keyword association on the images, we are able to accurately deduce and utilize the images' semantic contents for retrieval purposes. The accuracy and effectiveness of our method is demonstrated with experimental results on real-world image collections.
---
paper_title: Speed up interactive image retrieval

paper_content:

In multimedia retrieval, a query is typically interactively refined towards the "optimal" answers by exploiting user feedback. However, in existing work, in each iteration, the refined query is re-evaluated. This is not only inefficient but fails to exploit the answers that may be common between iterations. Furthermore, it may also take too many iterations to get the "optimal" answers. In this paper, we introduce a new approach called OptRFS (optimizing relevance feedback search by query prediction) for iterative relevance feedback search. OptRFS aims to take users to view the "optimal" results as fast as possible. It optimizes relevance feedback search by both shortening the searching time during each iteration and reducing the number of iterations. OptRFS predicts the potential candidates for the next iteration and maintains this small set for efficient sequential scan. By doing so, repeated candidate accesses (i.e., random accesses) can be saved, hence reducing the searching time for the next iteration. In addition, efficient scan on the overlap before the next search starts also tightens the search space with smaller pruning radius. As a step forward, OptRFS also predicts the "optimal" query, which corresponds to "optimal" answers, based on the early executed iterations' queries. By doing so, some intermediate iterations can be saved, hence reducing the total number of iterations. By taking the correlations among the early executed iterations into consideration, OptRFS investigates linear regression, exponential smoothing and linear exponential smoothing to predict the next refined query so as to decide the overlap of candidates between two consecutive iterations. Considering the special features of relevance feedback, OptRFS further introduces adaptive linear exponential smoothing to self-adjust the parameters for more accurate prediction. We implemented OptRFS and our experimental study on real life data sets show that it can reduce the total cost of relevance feedback search significantly. Some interesting features of relevance feedback search are also discovered and discussed.
---
paper_title: An Efficient Local Invariant Region Detector for Image Retrieval

paper_content:

An efficient algorithm for detecting invariant and salient image regions is presented. The detected regions are highly repeatable under various viewpoint and illumination variations. With the proposed implementation technique, the region detector becomes much more efficient and practical. Coupled with the distinctiveness and robustness of the detector, fast indexing and retrieval become possible.
---
paper_title: Learning of Relevance Feedback Using a Novel Kernel Based Neural Network

paper_content:

2 Abstract: In this paper, we introduce a novel neural network and fuzzy transaction based image retrieval system. The proposed system is a composite relevance feedback approach for image retrieval using semantic and visual learning. In semantic learning, the system integrates the log information of user feedback using a fuzzy feedback model to construct fuzzy repository. The repository remembers the user's intent and therefore, provides a better representation of each image in the database. The semantic similarity between the query image and each database image can then be computed using the current feedback and the semantic values in the fuzzy repository. In addition, 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 feature. This paper presents a novel neural network which is based on the nonlinear kernel Least Mean Square (KLMS). The proposed approach allows the users to select an initial query image and incrementally search target images via relevance feedback. If users aren't satisfied with the retrieved results, relevance feedback method is used to enhance the performance of the proposed system by updating a boundary for separating relevant images from irrelevant ones. These two similarity measures are normalized and combined together to form the overall similarity measure. Experimental results using a COREL database demonstrate the effectiveness of the proposed method.
---
paper_title: On-line content-based image retrieval system using joint querying and relevance feedback scheme

paper_content:

In a high-level semantic retrieval process, we utilize the search engine to retrieve a large number of images using a given text-based query. In a low-level image retrieval process, the system provides a similar image search function for the user to update the input query for image similarity characterization. This paper presents an On-line Content-Based Image Retrieval System using joint querying and relevance feedback scheme based on both high-level and low-level features. We also introduce fast and efficient color feature extraction namely auto color correlogram and correlation (ACCC) based on color correlogram (CC) and autocorrelogram (AC) algorithms, for extracting and indexing low-level features of images. To incorporate an image analysis algorithm into the text-based image search engines without degrading their response time, the framework of multi-threaded processing is proposed. The experimental evaluations based on coverage ratio measure show that our scheme significantly improves the retrieval performance of existing image search engine.
---
paper_title: Semi-supervised object recognition using flickr images

paper_content:

In this work we present an algorithm for extracting region level annotations from flickr images using a small set of manually labelled regions to guide the selection process. More specifically, we construct a set of flickr images that focuses on a certain concept and apply a novel graph based clustering algorithm on their regions. Then, we select the cluster or clusters that correspond to the examined concept guided by the manually labelled data. Experimental results show that although the obtained regions are of lower quality compared to the manually labelled regions, the gain in effort compensates for the loss in performance.
---
paper_title: Ensemble of Two-Class Classifiers for Image Annotation

paper_content:

Image annotation can be formulated as a multi-class classification problem. A multi-class classification problem can be solved by ensemble classifiers. We investigate the ensemble of multiple two-class classifiers based on MPEG-7 standard. To get ride of redundancy information, a binary-coded chromosome genetic algorithm is used to select individual optimal classification feature pattern for each two-class other than for all the classes. Two-class classifiers are generated based on the results of feature selection, and majority voting scheme is used to combine two-class classifiers. The experimental results over 2000 classified Corel images show that our approaches can improve annotation accuracies.
---
paper_title: Ontological Description of Image Content Using Regions Relationships

paper_content:

Much initial research on image annotation pays little attention to the spatial relationships between regions. Annotations are most frequently assigned globally and even when assigned locally the extraction of such relations is often neglected. For rich semantic description of visual information, it is essential to capture such relations. The aim of this research is to attempt and enhance annotation systems, either through automatic or semi-automatic means, by capturing the spatial relationships between regions or objects in images and incorporating such knowledge in a knowledge base such as ontology. As part of a preliminary experiment, a comparative analysis of three existing tools has been performed and the result obtained. This research will contribute a new approach to automate image linking to an ontology based on regions with automatic spatial relationships extraction between the composite regions or objects in images, which reliable with images in the real world.
---
paper_title: Transductive Multi-Instance Multi-Label learning algorithm with application to automatic image annotation

paper_content:

Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. Due to the inherent ambiguity of image-label mapping and the scarcity of training examples, the annotation task has become a challenge to systematically develop robust annotation models with better performance. From the perspective of machine learning, the annotation task fits both multi-instance and multi-label learning framework due to the fact that an image is usually described by multiple semantic labels (keywords) and these labels are often highly related to respective regions rather than the entire image. In this paper, we propose an improved Transductive Multi-Instance Multi-Label (TMIML) learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. The experiments over the well known Corel 5000 data set demonstrate that the proposed method is beneficial in the image annotation task and outperforms most existing image annotation algorithms.
---
paper_title: A review on automatic image annotation techniques

paper_content:

Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions
---
paper_title: A User-Driven Ontology Guided Image Retrieval Model

paper_content:

The demand for image retrieval and browsing online is growing dramatically. There are hundreds of millions of images available on the current World Wide Web. For multimedia documents, the typical keyword-based retrieval methods assume that the user has a specific goal in mind by using accurate query keywords in searching a set of images. Whereas the users may face with a repository of images whose domain is less known and content is semantically complicated, or the users may only generally know what they search for. In these cases it is difficult to decide what exact keywords to use for the query. In this article, we propose a user-centered image retrieval method that is based on the current Web, keyword-based annotation structure, and combining Ontology guided knowledge representation and probabilistic ranking. A prototype of web application for image retrieval using the proposed approach has been implemented. The model provides a recommendation subsystem to support and assist the user modifying the queries and reduces the userâ€™s cognitive load with the searching space. Experimental results show that the image retrieval recall and precision rates increased and therefore demonstrates the effectiveness of the model.
---
paper_title: Web Image Search by Automatic Image Annotation and Translation

paper_content:

There has been a growing interest in implementing online Web image search engine in the semantic level. However, most existing practical systems including popular commercial Web image search engines like Google and Yahoo! are either text- based or a simple hybrid of texts and visual features. This paper proposes a novel Web Image Search by Automatic Image Annotation and Translation (WISAIAT) system by using automatic image annotation and translation. We develop a technology which learns semantic image concepts from image contents and translates unstructured images into textual documents, so that images are indexed and retrieved in the same way as textual documents. Existing database management systems can be used to effectively manage image contents and image search can act as efficient as text search by translating images to textual documents through large scale machine learning. Experiments in both the Corel dataset and real Web dataset are performed to validate our system and the results are promising. This system suggests a new combination of texts and visual features to achieve a semantic Web image search and expected to become a reranking system to the existing Web image search result available online via the Internet.
---
paper_title: Ontology-Based Query Recommendation as a Support to Image Retrieval

paper_content:

Paper presented at the 19th Irish conference on Artificial Intelligence and Cognitive Science (AICS) 2008, 27-28 August, 2008, Cork
---
paper_title: Cross-media manifold learning for image retrieval & annotation

paper_content:

Fusion of visual content with textual information is an effective way for both content-based and keyword-based image retrieval. However, the performance of visual & textual fusion is affected greatly by the data noise and redundancy in both text (such as surrounding text in HTML pages) and visual (such as intra-class diversity) aspects. This paper presents a manifold-based cross-media optimization scheme to achieve visual & textual fusion within a unified framework. Cross-Media manifold co-training mechanism between Keyword-based Metric Space and Vision-Based Metric Space is proposed creatively to infer a best dual-space fusion by minimizing manifold-based visual & textual energy criterion. We present the Isomorphic Manifold Learning to map the annotation affection in image visual space onto keyword semantic space by manifold shrinkage. We also demonstrate its correctness and convergence from mathematical perspective. The retrieval can be performed using both keyword or sample images respectively on Keyword-Based Metric Space and Vision-Based Metric Space, while the simple distance classifiers will satisfy. Two groups of experiments are conducted: The first group is carried on Corel 5000 image database to validate our effectiveness by comparing with state-of-the-art Generalized Manifold Ranking Based Image Retrieval and SVM. The second group is done over real-world Flickr dataset with over 6,000 images to testify our effectiveness in real-world application. The promising results show that our model attains a significant improvement over state-of-the-art algorithms.
---
paper_title: Bridging the semantic gap in content-based image retrieval

paper_content:

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user's high-level concepts. This is known as the semantic gap problem. ::: This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features' distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. ::: The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. ::: The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques.
---
paper_title: Incorporating multiple SVMs for automatic image annotation

paper_content:

In this paper, a novel automatic image annotation system is proposed, which integrates two sets of support vector machines (SVMs), namely the multiple instance learning (MIL)-based and global-feature-based SVMs, for annotation. The MIL-based bag features are obtained by applying MIL on the image blocks, where the enhanced diversity density (DD) algorithm and a faster searching algorithm are applied to improve the efficiency and accuracy. They are further input to a set of SVMs for finding the optimum hyperplanes to annotate training images. Similarly, global color and texture features, including color histogram and modified edge histogram, are fed into another set of SVMs for categorizing training images. Consequently, two sets of image features are constructed for each test image and are, respectively, sent to the two sets of SVMs, whose outputs are incorporated by an automatic weight estimation method to obtain the final annotation results. Our proposed annotation approach demonstrates a promising performance for an image database of 12000 general-purpose images from COREL, as compared with some current peer systems in the literature.
---
paper_title: Automatic Semantic Annotation of Real-World Web Images

paper_content:

As the number of Web images is increasing at a rapid rate, searching them semantically presents a significant challenge. Many raw images are constantly uploaded with little meaningful direct annotations of semantic content, limiting their search and discovery. In this paper, we present a semantic annotation technique based on the use of image parametric dimensions and metadata. Using decision trees and rule induction, we develop a rule-based approach to formulate explicit annotations for images fully automatically, so that by the use of our method, semantic query such as "sunset by the sea in autumn in New York" can be answered and indexed purely by machine. Our system is evaluated quantitatively using more than 100,000 Web images. Experimental results indicate that this approach is able to deliver highly competent performance, attaining good recall and precision rates of sometimes over 80%. This approach enables a new degree of semantic richness to be automatically associated with images which previously can only be performed manually.
---
paper_title: CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines

paper_content:

We propose a content-based soft annotation (CBSA) procedure for providing images with semantical labels. The annotation procedure starts with labeling a small set of training images, each with one single semantical label (e.g., forest, animal, or sky). An ensemble of binary classifiers is then trained for predicting label membership for images. The trained ensemble is applied to each individual image to give the image multiple soft labels, and each label is associated with a label membership factor. To select a base binary-classifier for CBSA, we experiment with two learning methods, support vector machines (SVMs) and Bayes point machines (BPMs), and compare their class-prediction accuracy. Our empirical study on a 116-category 25K-image set shows that the BPM-based ensemble provides better annotation quality than the SVM-based ensemble for supporting multimodal image retrievals.
---
paper_title: Image annotation via graph learning

paper_content:

Image annotation has been an active research topic in recent years due to its potential impact on both image understanding and web image search. In this paper, we propose a graph learning framework for image annotation. First, the image-based graph learning is performed to obtain the candidate annotations for each image. In order to capture the complex distribution of image data, we propose a Nearest Spanning Chain (NSC) method to construct the image-based graph, whose edge-weights are derived from the chain-wise statistical information instead of the traditional pairwise similarities. Second, the word-based graph learning is developed to refine the relationships between images and words to get final annotations for each image. To enrich the representation of the word-based graph, we design two types of word correlations based on web search results besides the word co-occurrence in the training set. The effectiveness of the proposed solution is demonstrated from the experiments on the Corel dataset and a web image dataset.
---
paper_title: Modeling latent aspects for automatic image annotation

paper_content:

In this paper, we present an approach based on probabilistic latent semantic analysis (PLSA) to accomplish the tasks of automatic image annotation. In order to model training data precisely, we represent an image as a bag of visual words and employ two PLSA models to capture semantic information from visual and textual modalities respectively. Furthermore, an adaptive learning approach is proposed to combine the aspects learned from both modalities. For each image document, distribution over aspects is fused by different weight in terms of the entropy of its feature distribution. Consequently, the two models are linked with the same distribution over aspects. This structure can predict semantic annotation for an unseen image because it associates visual and textual modalities properly. We compare our approach with several previous approaches on a standard Corel dataset. The experiment results show that our approach performs more effectively and accurately.
---
paper_title: Image Annotation by Learning Rules from Regions Patterns

paper_content:

The modeling of multimedia and especially the semantic gap between the visual features and semantic concepts become an important domain due to the quantity of visual digital content, which speedily grows. In this paper, the analysis and semantic annotation of images are studied. The development of methods for colour image annotations based on learning represents the main contribution of the paper. The developed algorithms generate semantic pattern rules that identify high-level image concepts. A semantic pattern rule is a combination of images’ region patterns that identifies semantic concepts. Our methods are not limited to any specific domain and they can be applied in any field.
---
paper_title: Multimodal Image Retrieval Based on Annotation Keywords and Visual Content

paper_content:

Currently, most image retrieval systems use either purely visual features or textual metadata associated with images. They have advantages and disadvantages respectively. To overcome their drawbacks and improve the performance without sacrificing the efficiency, we propose the stepwise refinement multimodal image retrieval scheme based on annotation keywords and visual content, which can benefit from the strength of text- and content-based retrieval. The system starts query triggered by some keywords, and further refines the retrieval result based on blobs and regions information. The first step is to complete semantic filtering with weakening visual content, and the second step mainly considers existence and dependence of blobs, and the last step is to quantify the similarity in distribution and layout of visual content between the query image and candidate images by considering the weights of regions. The experiments show that proposed system outperforms the traditional image retrieval systems.
---
paper_title: An ontology approach to object-based image retrieval

paper_content:

In this paper, an image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions. Low-level features describing the color, position, size and shape of the resulting regions are extracted and are automatically mapped to appropriate intermediate-level descriptors forming a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) in a human-centered fashion. When querying, clearly irrelevant image regions are rejected using the intermediate-level descriptors; following that, a relevance feedback mechanism employing the low-level features is invoked to produce the final query results. The proposed approach bridges the gap between keyword-based approaches, which assume the existence of rich image captions or require manual evaluation and annotation of every image of the collection, and query-by-example approaches, which assume that the user queries for images similar to one that already is at his disposal.
---
paper_title: A latent image semantic indexing scheme for image retrieval on the web

paper_content:

In this paper, we present a novel latent image semantic indexing scheme for efficient retrieval of WWW images. We present a hierarchical image semantic structure called HIST, which captures image semantics in an ontology tree and visual features in a set of specific semantic domains. The query algorithm works in two phases. First, the ontology is used for quickly locating the relevant semantic domains. Second, within each semantic domain, the visual features are extracted, and similarity techniques are exploited to break the “dimensionality curse”. The target images can then be efficiently retrieved with high precision. The experimental results show that HIST achieves good query performance. Therefore, our method is promising in diverse Web image retrieval.
---
paper_title: Ontology driven content based image retrieval

paper_content:

Content based image retrieval (CBIR) methods are proposed as alternative or complementary solutions to keyword-based picture search. However these techniques mostly rely on low-level descriptors similarity between different items and when one uses such an application to find pictures, the proposed answers are often not conceptually similar to the query. In this paper, we describe RetrievOnto, an image retrieval (IR) system that allies CBIR techniques and semantics in order to better fit the users' expectations when querying an image database. The dataset is structured employing a term hierarchy, which is used to control the conceptual neighbourhood where similar items are searched. Only the leaf terms of the hierarchy have associated image sets but, with the use of the type-subtype relation between nodes, pictures are indirectly associated to all the concepts in the hierarchy and the system can propose localized IR processes, which associate low-level and conceptual similarities (on different levels of generality). We model a real-world situation by using pictures gathered from the Internet. The ontologically controlled IR method proposed in this paper is compared to classical CBIR functioning and we show that the introduction of a hierarchical structure improves precision results for the system.
---
paper_title: Image retrieval using a multilingual ontology

paper_content:

Search engines are among the most useful Internet applications. There exist several media types on the Web and, given the particularities of each of them, adapted search solutions are required. We limit our discussion to image search engines. While rapid and robust, existing image search engines offer results that respond only partially to the user's queries. An improvement of image search results might be obtained with the introduction of semantics in the dedicated systems. Here, we discuss the construction and the utilization of a multilingual lexical resources (WordNets in several languages) to improve image retrieval on the Internet. Given the initial nouns hierarchies in the WordNets, we build a multilingual OWL ontology including knowledge in English, Italian, and Spanish. A pictured representation of a dog remains a representation of a dog in spite of the associated name (would this be dog, perro or cane). The use of a large scale multilingual ontology allows us to offer the consequent sets of responses for the concepts in the hierarchy irrespective to the initial language the query was formulated in. With the use of an ontology to structure an image database, we can solve problems related to the ambiguity of a query content and we obtain an important gain in precision in the image sets rendered to the user compared to state of the art system.
---
paper_title: A Semantic Annotation Algorithm Based on Image Regional Object Ontology

paper_content:

The purpose of this paper is to research the image semantic auto-annotation method, which proposes an image semantic annotation method adopted ontology description and the image regional objects reasoning. First, the image semantic regional description model was build, and then the similar features of the region to achieve the similar semantics were annotated. Second, the algorithm focuses on the image regional object annotation description model which is related to the image semantic annotation description in the model, the spatial relation description relations between semantic objects, objects semantic relations description, abstract level structure and other key technologies, and the overall image semantic annotation would be achieved by semantic reasoning of regional objects. Finally, the search results showed that the method had better semantic retrieval performance.
---
paper_title: Improving Image Retrieval Using Semantic Resources

paper_content:

Many people use the Internet to find pictures of things. When extraneous images appear in response to simple queries on a search engine, the user has a hard time understanding why his seemingly clear request was not properly satisfied. If the computer could only understand what he wanted better, then maybe the results would be more precise. The introduction of an ontology, though hidden from the user, into current image retrieval engines may provide more accurate image responses to his query. The improvement of the results translates into the possibility of offering structured results, to disambiguate queries and to provide more interactivity options to the user, transforming the current string of character based retrieval into a concept based process. Each one of these aspects is presented and examples are used to support our proposals. We equally discuss the notion of picturability and justify our choice to work exclusively with entities that can be directly represented in a picture. Coordinating the use of a lexical ontology (an OWL representation of WordNet) with image processing techniques, we have developed a system that, given an initial query, automatically returns images associated with the query using automatic reformulation (each concepts is represented by its deepest hyponyms from the ontology). We show that picking randomly from this new set of pictures provides an improved representation for the initial, more general query. We also treat the visual aspects of the images for these deepest hyponyms (the leaves of WordNet). The depictions associated to leaf categories are clustered into coherent sets using low-level image features like color and texture. Some limitations (e.g. the quality and coverage of the semantic structure, the impossibility to answer complex queries) of the ontology based retrieval are equally discussed.
---
paper_title: Ontology enhanced web image retrieval: aided by wikipedia & spreading activation theory

paper_content:

Ontology, as an effective approach to bridge the semantic gap in various domains, has attracted a lot of interests from multimedia researchers. Among the numerous possibilities enabled by ontology, we are particularly interested in exploiting ontology for a better understanding of media task (particularly, images) on the World Wide Web. To achieve our goal, two open issues are inevitably involved: 1) How to avoid the tedious manual work for ontology construction? 2) What are the effective inference models when using an ontology? Recent works[11, 16] about ontology learned from Wikipedia has been reported in conferences targeting the areas of knowledge management and artificial intelligent. There are also reports of different inference models being investigated [5, 13, 15]. However, so far there has not been any comprehensive solution. In this paper, we look at these challenges and attempt to provide a general solution to both questions. Through a careful analysis of the online encyclopedia Wikipedia's categorization and page content, we choose it as our knowledge source and propose an automatic ontology construction approach. We prove that it is a viable way to build ontology under various domains. To address the inference model issue, we provide a novel understanding of the ontology and consider it as a type of semantic network, which is similar to brain models in the cognitive research field. Spreading Activation Techniques, which have been proved to be a correct information processing model in the semantic network, are consequently introduced for inference. We have implemented a prototype system with the developed solutions for web image retrieval. By comprehensive experiments on the canine category of the animal kingdom, we show that this is a scalable architecture for our proposed methods.
---
paper_title: Does ontology help in image retrieval?: a comparison between keyword, text ontology and multi-modality ontology approaches

paper_content:

Ontologies are effective for representing domain concepts and relations in a form of semantic network. Many efforts have been made to import ontology into information matchmaking and retrieval. This trend is further accelerated by the convergence of various high-level concepts and low-level features supported by ontologies. In this paper we propose a comparison between traditional keyword based image retrieval and the promising ontology based image retrieval. To be complete, we construct the ontologies not only on text annotation, but also on a combination of text annotation and image feature. The experiments are conducted on a medium-sized data set including about 4000 images. The result proved the efficacy of utilizing both text and image features in a multi modality ontology to improve the image retrieval.
---
paper_title: A Hybrid Model of Image Retrieval Based on Ontology Technology and Probabilistic Ranking

paper_content:

There are hundreds of millions of images available on the current World Wide Web. The demand for image retrieval online is growing dramatically. For multimedia documents, the typical keyword-based retrieval method has encountered problems mainly in the areas of: 1) the quality of the search result; 2) the usage of the system. With the advent and development of the Semantic Web, information retrieval can widely take advantage of this technology which is expected as the next generation of internet. However, before shifting up to the Semantic Web generation, there are still numerous resources on the current Web without semantic annotation. In this paper, we propose a hybrid retrieval method which is based on the current Web, keyword-based annotation structure, and combining Ontology-guided reasoning and probabilistic ranking. A Web application for image retrieval using our proposed approach has been implemented. Furthermore, the system offers recommendations to the user to demonstrate the effectiveness of the model. Experimental results show that the image retrieval recall and precision rates increase by using the proposed hybrid approach.
---
paper_title: Ontology enhanced web image retrieval: aided by wikipedia & spreading activation theory

paper_content:

Ontology, as an effective approach to bridge the semantic gap in various domains, has attracted a lot of interests from multimedia researchers. Among the numerous possibilities enabled by ontology, we are particularly interested in exploiting ontology for a better understanding of media task (particularly, images) on the World Wide Web. To achieve our goal, two open issues are inevitably involved: 1) How to avoid the tedious manual work for ontology construction? 2) What are the effective inference models when using an ontology? Recent works[11, 16] about ontology learned from Wikipedia has been reported in conferences targeting the areas of knowledge management and artificial intelligent. There are also reports of different inference models being investigated [5, 13, 15]. However, so far there has not been any comprehensive solution. In this paper, we look at these challenges and attempt to provide a general solution to both questions. Through a careful analysis of the online encyclopedia Wikipedia's categorization and page content, we choose it as our knowledge source and propose an automatic ontology construction approach. We prove that it is a viable way to build ontology under various domains. To address the inference model issue, we provide a novel understanding of the ontology and consider it as a type of semantic network, which is similar to brain models in the cognitive research field. Spreading Activation Techniques, which have been proved to be a correct information processing model in the semantic network, are consequently introduced for inference. We have implemented a prototype system with the developed solutions for web image retrieval. By comprehensive experiments on the canine category of the animal kingdom, we show that this is a scalable architecture for our proposed methods.
---
paper_title: An Image Clustering and Retrieval Framework Using Feedback-Based Integrated Region Matching

paper_content:

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target especially when more than one object of interest is involved in the retrieval. In this paper, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in reflecting users’ retrieval interests in object-based image retrieval.
---
paper_title: A Hybrid Model of Image Retrieval Based on Ontology Technology and Probabilistic Ranking

paper_content:

There are hundreds of millions of images available on the current World Wide Web. The demand for image retrieval online is growing dramatically. For multimedia documents, the typical keyword-based retrieval method has encountered problems mainly in the areas of: 1) the quality of the search result; 2) the usage of the system. With the advent and development of the Semantic Web, information retrieval can widely take advantage of this technology which is expected as the next generation of internet. However, before shifting up to the Semantic Web generation, there are still numerous resources on the current Web without semantic annotation. In this paper, we propose a hybrid retrieval method which is based on the current Web, keyword-based annotation structure, and combining Ontology-guided reasoning and probabilistic ranking. A Web application for image retrieval using our proposed approach has been implemented. Furthermore, the system offers recommendations to the user to demonstrate the effectiveness of the model. Experimental results show that the image retrieval recall and precision rates increase by using the proposed hybrid approach.
---

Title: A Literature Review of Image Retrieval based on Semantic Concept

Section 1: INTRODUCTION
Description 1: This section provides an overview of image retrieval methods, their limitations, and the motivation behind content-based image retrieval (CBIR).

Section 2: IMAGE RETRIEVAL APPROACHES
Description 2: This section details the various methods for image retrieval, including text-based, content-based, and hybrid methods.

Section 3: Text-Based Image Retrieval (TBIR)
Description 3: This section describes the text-based image retrieval method, its working mechanisms, and associated challenges.

Section 4: Content-Based Image Retrieval (CBIR)
Description 4: This section explains the CBIR technique focused on low-level visual features, its benefits, and issues related to semantic interpretation of images.

Section 5: Hybrid Method for Image Retrieval
Description 5: This section discusses the integration of textual and visual features in hybrid image retrieval methods and its potential advantages.

Section 6: SEMANTIC GAP PROBLEM
Description 6: This section delves into the semantic gap issue in CBIR, explaining why it occurs and its impacts on image retrieval effectiveness.

Section 7: Relevance Feedback (RF)
Description 7: This section explores the relevance feedback approach, including its steps and limitations in reducing the semantic gap.

Section 8: Automatic Image Annotation
Description 8: This section discusses automatic image annotation methods, particularly keyword-based and ontology-based approaches.

Section 9: Keywords-based Annotation
Description 9: This section examines the keyword-based annotation method, its process, and associated challenges.

Section 10: Ontology-based Annotation
Description 10: This section elaborates on the ontology-based annotation method, its principles, and challenges.

Section 11: LIMITATIONS OF CURRENT ONTOLOGY BASED IMAGE RETRIEVAL SYSTEMS
Description 11: This section identifies and discusses the limitations present in current ontology-based image retrieval systems.

Section 12: FUTURE DIRECTIONS FOR IMAGE RETRIEVAL SYSTEMS
Description 12: This section outlines potential future directions to enhance image retrieval systems, including automatic segmentation, semantic annotation, and ontology reasoning.

Section 13: CONCLUSION
Description 13: This section summarizes the review, discussing the contributions and limitations of various image retrieval systems and providing recommendations for future research.