Hybrid image representation methods for automatic image annotation: A survey
---
paper_title: TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation

paper_content:

Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images are predicted using a weighted nearest-neighbor model to exploit labeled training images. Neighbor weights are based on neighbor rank or distance. TagProp allows the integration of metric learning by directly maximizing the log-likelihood of the tag predictions in the training set. In this manner, we can optimally combine a collection of image similarity metrics that cover different aspects of image content, such as local shape descriptors, or global color histograms. We also introduce a word specific sigmoidal modulation of the weighted neighbor tag predictions to boost the recall of rare words. We investigate the performance of different variants of our model and compare to existing work. We present experimental results for three challenging data sets. On all three, TagProp makes a marked improvement as compared to the current state-of-the-art.
---
paper_title: Texture orientation for sorting photos "at a glance"

paper_content:

Investigates a measure of "dominant perceived orientation" that has been developed to match the output of a human study involving 40 subjects. The results of this measure are compared with humans analyzing seven "teaser" images to test its effectiveness for finding perceptually dominant orientations. The use of low-level orientation is then applied to a "quick search" problem important in image database applications. Since both pigeons and humans are able to perform coarse classification of certain kinds of scenes, e.g., city from country, without taking time or brain-power to solve the image understanding problem, the authors conjecture that the collective behavior of low-level textural features such as orientation may be doing most of the work. The authors demonstrate a simple test of global multiscale orientation for quickly searching a database of vacation photos for likely "city/suburb" shots. The orientation features achieve agreement with human classification in 91 out of 98 of the scenes.
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paper_title: A Study on Image Annotation Techniques

paper_content:

With the development of mobile device, large amount of digital images are generated every day. The requirements of effective indexing and searching image are growing rapidly. Image annotation is an effective way for content based image retrieval. This paper gives a review of different categories for image annotation. The paper also gives a brief overview of different methods for image annotation under SemiAutomatic and Automatic annotations. Manual annotation is a costly and time consuming work, especially for mobile device. General Terms Context, Image annotation, Retrieval.
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paper_title: An image retrieval system by impression words and specific object names-IRIS

paper_content:

Abstract In this paper, we describe a new image retrieval system for scenery images named IRIS (Image Retrieval by Impression words and Specific object names) which uses specific object names in addition to impression words which can reflect ambiguous human kansei (impression and sensitivity) as the retrieval keywords. IRIS has not only the function which retrieves images but also that can estimate keywords to images automatically. In IRIS, first an image is divided into some regions. Next each region is roughly classified into the sky, earth and water categories by the image recognition method using a neural network. Then the image characteristics are extracted from each category, and the impression words are given to the image automatically. After the regions are classified into sky/earth/water categories, they are classified into much more detailed objects such as mountain or cloudy weather sky. By the hierarchical recognition, the specific object names are given to an image automatically. Using concrete object names with vague impression words as keywords, flexible retrieval is possible. We carried out extensive experiments to test the performance of the developed IRIS. The retrieval experiments has shown the efficiency of retrieval when the impression words and the specific object names are used together. Also, they have shown the high retrieval performances of IRIS.
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paper_title: Modeling annotated data

paper_content:

We consider the problem of modeling annotated data---data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models which aim to describe such data, culminating in correspondence latent Dirichlet allocation, a latent variable model that is effective at modeling the joint distribution of both types and the conditional distribution of the annotation given the primary type. We conduct experiments on the Corel database of images and captions, assessing performance in terms of held-out likelihood, automatic annotation, and text-based image retrieval.
---
paper_title: Automated image annotation using global features and robust nonparametric density estimation

paper_content:

This paper describes a simple framework for automatically annotating images using non-parametric models of distributions of image features. We show that under this framework quite simple image properties such as global colour and texture distributions provide a strong basis for reliably annotating images. We report results on subsets of two photographic libraries, the Corel Photo Archive and the Getty Image Archive. We also show how the popular Earth Mover’s Distance measure can be effectively incorporated within this framework.
---
paper_title: A computationally efficient approach to indoor/outdoor scene classification

paper_content:

Prior research in scene classification has shown that high-level information can be inferred from low-level image features. Classification rates of roughly 90% have been reported using low-level features to predict indoor scenes vs. outdoor scenes. However, the high classification rates are often achieved by using computationally expensive, high-dimensional feature sets, thus limiting the practical implementation of such systems. We show that a more computationally efficient approach to indoor/outdoor classification can yield classification rates comparable to the best methods reported in the literature. A low complexity, low-dimensional feature set is used in conjunction with a two-stage support vector machine classification scheme to achieve a classification rate of 90.2% on a large database of consumer photographs.
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paper_title: Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach

paper_content:

Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in computer vision and content-based image retrieval. In this paper, we introduce a statistical modeling approach to this problem. Categorized images are used to train a dictionary of hundreds of statistical models each representing a concept. Images of any given concept are regarded as instances of a stochastic process that characterizes the concept. To measure the extent of association between an image and the textual description of a concept, the likelihood of the occurrence of the image based on the characterizing stochastic process is computed. A high likelihood indicates a strong association. In our experimental implementation, we focus on a particular group of stochastic processes, that is, the two-dimensional multiresolution hidden Markov models (2D MHMMs). We implemented and tested our ALIP (Automatic Linguistic Indexing of Pictures) system on a photographic image database of 600 different concepts, each with about 40 training images. The system is evaluated quantitatively using more than 4,600 images outside the training database and compared with a random annotation scheme. Experiments have demonstrated the good accuracy of the system and its high potential in linguistic indexing of photographic images.
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paper_title: A Max Margin Framework on Image Annotation and Multimodal Image Retrieval

paper_content:

This paper presents a max margin framework on image annotation and multimodal image retrieval as a structured prediction model. Following the max margin approach the image retrieval problem is formulated as a quadratic programming problem. By properly selecting joint feature representation between different modalities, our framework captures the dependency information between different modalities and avoids retraining the model from scratch when database undergoes dynamic updates. While this framework is a general approach which can be applied to multimodal information retrieval in any domains, we apply this approach to the Berkeley Drosophila embryo image database for the evaluation purpose. Experimental results show significant performance improvements over a state-of-the-art method.
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paper_title: Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes

paper_content:

Curve matching is one instance of the fundamental correspondence problem. Our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations. A syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming. We present extensive experiments where we apply the algorithm to silhouette matching. In these experiments, we examine partial occlusion, viewpoint variation, articulation, and class matching (where silhouettes of similar objects are matched). Based on the qualitative syntactic matching, we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images. We use this experiment to objectively evaluate our algorithm. First, we compare our results to those reported by others. Second, we use the dissimilarity values in order to organize the image database into shape categories. The veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation.
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paper_title: End-User Searching Challenges Indexing Practices in the Digital Newspaper Photo Archive

paper_content:

Previous research in conceptual indexing methods of images has furnished us with refined theoretical frameworks characterising various aspects of images that could and should be indexed using textual descriptors. The development of digital image processing technologies has bred a brigade of content-based indexing and retrieval methods available for applications. What the users need and in what kinds of environments different indexing and retrieval methods are relevant, has remained an area of less intensive research work.This article presents the results of a field study concentrating on journalists as users of a digital newspaper photo archive. The expressed photo needs, applied selection criteria and observed searching behaviours in journalists' daily work were contrasted with the indexing practices applied by the archivists. The results showed that the journalists achieved satisfactory results when trivial query terms were available, e.g. when photos of named persons were needed. Browsing was the main searching strategy applied by the journalists, but the system did not support browsing well. The access problems faced by the users in particular photo needs are discussed in detail. The paper concludes by discussing the potential approaches in developing both the concept-based and content-based indexing methods as well as the user interfaces in photo retrieval systems.
---
paper_title: Support vector machines for histogram-based image classification

paper_content:

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM's) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y) = e(-rho)Sigma(i)/xia-yia/b with a < or = 1 and b < or = 2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input x(i)-->x(i)(a) improves the performance of linear SVM's to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.
---
paper_title: Region based image annotation through a training image based feature space

paper_content:

A large box of sufficient size to carry a coat is cut and folded with the use of a conventionally sized standard die from a single piece of sheet-form material and has side panel structures formed by having an exterior side panel, an integral hinge, and an interior side panel with latching tabs to interlock with the bottom of the box. The exterior and interior side panels have a reduced total height due to the hinged side panels being formed with a bias cut so as to reduce the effective height of the panels to be less than the total height of the completed box while still providing closure integrity and structural stability.
---
paper_title: Automatically Annotating Images with Keywords: A Review of Image Annotation Systems

paper_content:

There are disclosed carbonate polymers having ethenyl aryl moieties. Such carbonate polymers are prepared from one or more multi-hydric compounds and have an average degree of polymerization of at least about 1 based on multi-hydric compound. These polymers, including blends thereof, can be easily processed and shaped into various forms and structures according to the known techniques. During or subsequent to the processing, the polymers can be crosslinked, by exposure to heat or radiation, for example, to provide crosslinked polymer compositions. These compositions have a good combination of properties, including for example, processability into shaped articles having unexpectedly good combinations of toughness, solvent resistance, ignition resistance, modulus and resistance to thermal linear expansion.
---
paper_title: A new baseline for image annotation

paper_content:

Automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. Many techniques have been proposed for image annotation in the last decade that give reasonable performance on standard datasets. However, most of these works fail to compare their methods with simple baseline techniques to justify the need for complex models and subsequent training. In this work, we introduce a new baseline technique for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes low-level image features and a simple combination of basic distances to find nearest neighbors of a given image. The keywords are then assigned using a greedy label transfer mechanism. The proposed baseline outperforms the current state-of-the-art methods on two standard and one large Web dataset. We believe that such a baseline measure will provide a strong platform to compare and better understand future annotation techniques.
---
paper_title: SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

paper_content:

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images.
---
paper_title: Image classification for content-based indexing

paper_content:

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier.
---
paper_title: Learning the semantics of words and pictures

paper_content:

We present a statistical model for organizing image collections which integrates semantic information provided by associate text and visual information provided by image features. The model is very promising for information retrieval tasks such as database browsing and searching for images based on text and/or image features. Furthermore, since the model learns relationships between text and image features, it can be used for novel applications such as associating words with pictures, and unsupervised learning for object recognition.
---
paper_title: Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic analysis

paper_content:

In this paper, we present a novel algorithm to classify UAV images through the image annotation which is a ::: semi-supervised method. During the annotation process, we first divide whole image into different sizes of blocks and ::: generate suitable visual words which are the K-means clustering centers or just pixels in small size image block. Then, ::: given a set of image blocks for each semantic concept as training data, learning is based on the Probabilistic Latent ::: Semantic Analysis (PLSA). The probability distributions of visual words in every document can be learned through the ::: PLSA model. The labeling of every document (image block) is done by computing the similarity of its feature ::: distribution to the distribution of the training documents with the Kullback-Leibler (K-L) divergence. Finally, the ::: classification of the UAV images will be done by combining all the image blocks in every block size. The UAV images ::: using in our experiments was acquired during Sichuan earthquake in 2008. The results show that smaller size block ::: image will get better classification results.
---
paper_title: Image information systems: where do we go from here?

paper_content:

A conceptual framework for image information systems is presented. Current research topics are surveyed, and application examples are presented. The design issues for the next generation of active image systems are discussed. It is suggested that the next generation of active image information systems should be designed on the basis of notions of generalized icons and active indexes, resulting in smart images. >
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paper_title: Image Classification and Querying using Composite Region Templates

paper_content:

The tremendous growth in digital imagery is driving the need for more sophisticated methods for automatic image analysis, cataloging, and searching. We present a method for classifying and querying images based on the spatial orderings of regions or objects using composite region templates (CRTs). The CRTs capture the spatial information statistically and provide a robust way to measure similarity in the presence of region insertions, deletions, substitutions, replications, and relocations. The CRTs can be used for classifying and annotating images by assigning symbols to the regions or objects and by extracting symbol strings from spatial scans of the images. The symbol strings can be decoded using a library of annotated CRTs to automatically label and classify the images. The CRTs can also be used for searching by sketch or example by measuring image similarity based on relative counts of the CRTs.
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paper_title: Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

paper_content:

We describe a model of object recognition as machine translation. In this model, recognition is a process of annotating image regions with words. Firstly, images are segmented into regions, which are classified into region types using a variety of features. A mapping between region types and keywords supplied with the images, is then learned, using a method based around EM. This process is analogous with learning a lexicon from an aligned bitext. For the implementation we describe, these words are nouns taken from a large vocabulary. On a large test set, the method can predict numerous words with high accuracy. Simple methods identify words that cannot be predicted well. We show how to cluster words that individually are difficult to predict into clusters that can be predicted well -- for example, we cannot predict the distinction between train and locomotive using the current set of features, but we can predict the underlying concept. The method is trained on a substantial collection of images. Extensive experimental results illustrate the strengths and weaknesses of the approach.
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paper_title: Content-based retrieval in multimedia imaging

paper_content:

Content-based retrieval is founded on neural networks, this technology allows automatic filing of images and a wide range of possible queries of the resulting database. This is in contrast to methods such as entering SQL keys manually for each image as it is filed and later correctly re-entering those keys to retrieve the same image. An SQL-based approach does not take into account information that is hard to describe with text, such as sounds and images. Neural networks can be trained to translate `noisy' or chaotic image data into simpler, more reliable feature sets. By converting the images into the level of abstraction necessary for symbolic processing, standard database indexing methods can then be applied, or used in layers of associative database neural networks directly.
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paper_title: Region based image annotation through multiple-instance learning

paper_content:

In an annotated image database, keywords are usually associated with images instead of individual regions, which poses a major challenge for any region based image annotation algorithm. In this paper, we propose to learn the correspondence between image regions and keywords through Multiple-Instance Learning (MIL). After a representative image region has been learned for a given keyword, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. The classification problem is then addressed using the Bayesian framework. The proposed image annotation method is evaluated on an image database with 5,000 images.
---
paper_title: Indoor-outdoor image classification

paper_content:

We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features of: histograms in the Ohta color space; multiresolution, simultaneous autoregressive model parameters; and coefficients of a shift-invariant DCT. We demonstrate that performance is improved by computing features on subblocks, classifying these subblocks, and then combining these results in a way reminiscent of stacking. State of the art single-feature methods are shown to result in about 75-86% performance, while the new method results in 90.3% correct classification, when evaluated on a diverse database of over 1300 consumer images provided by Kodak.
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paper_title: Image annotation by incorporating word correlations into multi-class SVM

paper_content:

Image annotation systems aim at automatically annotating images with semantic keywords. Machine learning approaches are often used to develop these systems. In this paper, we propose an image annotation approach by incorporating word correlations into multi-class support vector machine (SVM). At first, each image is segmented into five fixed-size blocks instead of time-consuming object segmentation. Every keyword from training images is manually assigned to the corresponding block and word correlations are computed by a co-occurrence matrix. Then, MPEG-7 visual descriptors are applied to these blocks to represent visual features and the minimal-redundancy-maximum-relevance (mRMR) method is used to reduce the feature dimension. A block-feature-based multi-class SVM classifier is trained for 80 semantic concepts. At last, the probabilistic outputs from SVM and the word correlations are integrated to obtain the final annotation keywords. The experiments on Corel 5000 dataset demonstrate our approach is effective and efficient.
---
paper_title: Automatic image annotation and object detection

paper_content:

We live in the midst of the information era, during which organising and indexing information more effectively is a matter of essential importance. With the fast development of digital imagery, how to search images - a rich form of information - more efficiently by their content has become one of the biggest challenges. Content-based image retrieval (CBIR) has been the traditional and dominant technique for searching images for decades. However, not until recently have researchers started to realise some vital problems existing in CBIR systems. One of the most important is perhaps what people call the \textit{semantic gap}, which refers to the gap between the information that can be extracted from images and the interpretation of the images for humans. As an attempt to bridge the semantic gap, automatic image annotation has been gaining more and more attentions in recent years. This thesis aims to explore a number of different approaches to automatic image annotation and some related issues. It begins with an introduction into different techniques for image description, which forms the foundation of the research on image auto-annotation. The thesis then goes on to give an in-depth examination of some of the quality issues of the data-set used for evaluating auto-annotation systems. A series of approaches to auto-annotation are presented in the follow-up chapters. Firstly, we describe an approach that incorporates the salient based image representation into a statistical model for better annotation performance. Secondly, we explore the use of non-negative matrix factorisation (NMF), a matrix decomposition tehcnique, for two tasks; object class detection and automatic annotation of images. The results imply that NMF is a promising sub-space technique for these purposes. Finally, we propose a model named the image based feature space (IBFS) model for linking image regions and keywords, and for image auto-annotation. Both image regions and keywords are mapped into the same space in which their relationships can be measured. The idea of multiple segmentations is then implemented in the model, and better results are achieved than using a single segmentation.
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paper_title: Integration of color, edge, shape and texture features for automatic region-based image annotation and retrieval

paper_content:

We present algorithms for automatic image annotation and retrieval based on color, shape, texture, and any combination of two or more of these features. Pixel- or region (object)-based color; region-based shape; and block- or region-based texture features have been considered. Automatic region selection has been accom- plished by integrating color and spatial edge features. Color, shape, and texture indexing may be knowledge based (using appropriate training sets) or by example. The multifeature integration algorithms are designed to: (i) offer the user a wide range of options and flex- ibilities in order to enhance the outcome of the search and retrieval operations, and (ii) provide a compromise between accuracy and computational complexity, and vice versa. We demonstrate the per- formance of the proposed algorithms on a variety of images. © 1998 SPIE and IS&T. (S1017-9909(98)02603-8)
---
paper_title: TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation

paper_content:

Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images are predicted using a weighted nearest-neighbor model to exploit labeled training images. Neighbor weights are based on neighbor rank or distance. TagProp allows the integration of metric learning by directly maximizing the log-likelihood of the tag predictions in the training set. In this manner, we can optimally combine a collection of image similarity metrics that cover different aspects of image content, such as local shape descriptors, or global color histograms. We also introduce a word specific sigmoidal modulation of the weighted neighbor tag predictions to boost the recall of rare words. We investigate the performance of different variants of our model and compare to existing work. We present experimental results for three challenging data sets. On all three, TagProp makes a marked improvement as compared to the current state-of-the-art.
---
paper_title: Automated image annotation using global features and robust nonparametric density estimation

paper_content:

This paper describes a simple framework for automatically annotating images using non-parametric models of distributions of image features. We show that under this framework quite simple image properties such as global colour and texture distributions provide a strong basis for reliably annotating images. We report results on subsets of two photographic libraries, the Corel Photo Archive and the Getty Image Archive. We also show how the popular Earth Mover’s Distance measure can be effectively incorporated within this framework.
---
paper_title: Support vector machines for histogram-based image classification

paper_content:

Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM's) can generalize well on difficult image classification problems where the only features are high dimensional histograms. Heavy-tailed RBF kernels of the form K(x, y) = e(-rho)Sigma(i)/xia-yia/b with a < or = 1 and b < or = 2 are evaluated on the classification of images extracted from the Corel stock photo collection and shown to far outperform traditional polynomial or Gaussian radial basis function (RBF) kernels. Moreover, we observed that a simple remapping of the input x(i)-->x(i)(a) improves the performance of linear SVM's to such an extend that it makes them, for this problem, a valid alternative to RBF kernels.
---
paper_title: A new baseline for image annotation

paper_content:

Automatically assigning keywords to images is of great interest as it allows one to index, retrieve, and understand large collections of image data. Many techniques have been proposed for image annotation in the last decade that give reasonable performance on standard datasets. However, most of these works fail to compare their methods with simple baseline techniques to justify the need for complex models and subsequent training. In this work, we introduce a new baseline technique for image annotation that treats annotation as a retrieval problem. The proposed technique utilizes low-level image features and a simple combination of basic distances to find nearest neighbors of a given image. The keywords are then assigned using a greedy label transfer mechanism. The proposed baseline outperforms the current state-of-the-art methods on two standard and one large Web dataset. We believe that such a baseline measure will provide a strong platform to compare and better understand future annotation techniques.
---
paper_title: SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

paper_content:

We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05%(±0.56%) at 15 training images per class, and 66.23%(±0.48%) at 30 training images.
---
paper_title: Image classification for content-based indexing

paper_content:

Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified as indoor or outdoor; outdoor images are further classified as city or landscape; finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small vector quantizer (whose optimal size is selected using a modified MDL criterion) can be used to model the class-conditional densities of the features, required by the Bayesian methodology. The classifiers have been designed and evaluated on a database of 6931 vacation photographs. Our system achieved a classification accuracy of 90.5% for indoor/outdoor, 95.3% for city/landscape, 96.6% for sunset/forest and mountain, and 96% for forest/mountain classification problems. We further develop a learning method to incrementally train the classifiers as additional data become available. We also show preliminary results for feature reduction using clustering techniques. Our goal is to combine multiple two-class classifiers into a single hierarchical classifier.
---
paper_title: Texture orientation for sorting photos "at a glance"

paper_content:

Investigates a measure of "dominant perceived orientation" that has been developed to match the output of a human study involving 40 subjects. The results of this measure are compared with humans analyzing seven "teaser" images to test its effectiveness for finding perceptually dominant orientations. The use of low-level orientation is then applied to a "quick search" problem important in image database applications. Since both pigeons and humans are able to perform coarse classification of certain kinds of scenes, e.g., city from country, without taking time or brain-power to solve the image understanding problem, the authors conjecture that the collective behavior of low-level textural features such as orientation may be doing most of the work. The authors demonstrate a simple test of global multiscale orientation for quickly searching a database of vacation photos for likely "city/suburb" shots. The orientation features achieve agreement with human classification in 91 out of 98 of the scenes.
---
paper_title: A computationally efficient approach to indoor/outdoor scene classification

paper_content:

Prior research in scene classification has shown that high-level information can be inferred from low-level image features. Classification rates of roughly 90% have been reported using low-level features to predict indoor scenes vs. outdoor scenes. However, the high classification rates are often achieved by using computationally expensive, high-dimensional feature sets, thus limiting the practical implementation of such systems. We show that a more computationally efficient approach to indoor/outdoor classification can yield classification rates comparable to the best methods reported in the literature. A low complexity, low-dimensional feature set is used in conjunction with a two-stage support vector machine classification scheme to achieve a classification rate of 90.2% on a large database of consumer photographs.
---
paper_title: Automatically Annotating Images with Keywords: A Review of Image Annotation Systems

paper_content:

There are disclosed carbonate polymers having ethenyl aryl moieties. Such carbonate polymers are prepared from one or more multi-hydric compounds and have an average degree of polymerization of at least about 1 based on multi-hydric compound. These polymers, including blends thereof, can be easily processed and shaped into various forms and structures according to the known techniques. During or subsequent to the processing, the polymers can be crosslinked, by exposure to heat or radiation, for example, to provide crosslinked polymer compositions. These compositions have a good combination of properties, including for example, processability into shaped articles having unexpectedly good combinations of toughness, solvent resistance, ignition resistance, modulus and resistance to thermal linear expansion.
---
paper_title: Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic analysis

paper_content:

In this paper, we present a novel algorithm to classify UAV images through the image annotation which is a ::: semi-supervised method. During the annotation process, we first divide whole image into different sizes of blocks and ::: generate suitable visual words which are the K-means clustering centers or just pixels in small size image block. Then, ::: given a set of image blocks for each semantic concept as training data, learning is based on the Probabilistic Latent ::: Semantic Analysis (PLSA). The probability distributions of visual words in every document can be learned through the ::: PLSA model. The labeling of every document (image block) is done by computing the similarity of its feature ::: distribution to the distribution of the training documents with the Kullback-Leibler (K-L) divergence. Finally, the ::: classification of the UAV images will be done by combining all the image blocks in every block size. The UAV images ::: using in our experiments was acquired during Sichuan earthquake in 2008. The results show that smaller size block ::: image will get better classification results.
---
paper_title: Indoor-outdoor image classification

paper_content:

We show how high-level scene properties can be inferred from classification of low-level image features, specifically for the indoor-outdoor scene retrieval problem. We systematically studied the features of: histograms in the Ohta color space; multiresolution, simultaneous autoregressive model parameters; and coefficients of a shift-invariant DCT. We demonstrate that performance is improved by computing features on subblocks, classifying these subblocks, and then combining these results in a way reminiscent of stacking. State of the art single-feature methods are shown to result in about 75-86% performance, while the new method results in 90.3% correct classification, when evaluated on a diverse database of over 1300 consumer images provided by Kodak.
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paper_title: Image annotation by incorporating word correlations into multi-class SVM

paper_content:

Image annotation systems aim at automatically annotating images with semantic keywords. Machine learning approaches are often used to develop these systems. In this paper, we propose an image annotation approach by incorporating word correlations into multi-class support vector machine (SVM). At first, each image is segmented into five fixed-size blocks instead of time-consuming object segmentation. Every keyword from training images is manually assigned to the corresponding block and word correlations are computed by a co-occurrence matrix. Then, MPEG-7 visual descriptors are applied to these blocks to represent visual features and the minimal-redundancy-maximum-relevance (mRMR) method is used to reduce the feature dimension. A block-feature-based multi-class SVM classifier is trained for 80 semantic concepts. At last, the probabilistic outputs from SVM and the word correlations are integrated to obtain the final annotation keywords. The experiments on Corel 5000 dataset demonstrate our approach is effective and efficient.
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paper_title: Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

paper_content:

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which combines both global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from some spatially partitioned image patches by Gabor wavelet transform. After this, multiple classifiers are obtained by applying Fisher Discriminant Analysis on global Fourier features and local patches of Gabor features. All these classifiers are combined to form a hierarchical ensemble by sum rule. We evaluated the proposed method using Face Recognition Grand Challenge (FRGC) experimental protocols and database known as the largest data sets available. Experimental results on FRGC version 2.0 data set have shown that the proposed method achieves a verification rate of 86%, while the best reported was 76%.
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paper_title: An efficient face recognition through combining local features and statistical feature extraction

paper_content:

This paper proposes a hybrid method for face recognition using local features and statistical feature extraction methods. First, a dense set of local feature points are extracted in order to represent a facial image. Each local feature point is described by the keypoint descriptor defined by SIFT feature. Then, the statistical feature extraction methods, PCA and LDA, are applied to the set of local feature descriptors in order to find low dimensional features. With the obtained low dimensional feature vectors, we can conduct face recognition task efficiently using a simple classifier. Through computational experiments on benchmark data sets, we show that the proposed method is superior to the conventional PCA and LDA in the classification performance. In addition, we also show that the proposed method can achieve remarkable improvement in the processing time compared to the conventional keypoint matching methods proposed for local features.
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paper_title: A study of block-global feature based supervised image annotation

paper_content:

In order to get better semantic annotation performance, block-global features are extracted as low-level visual features for image semantic annotation. Specifically, wellknown global feature extraction method, namely two-dimensional principal component analysis (2DPCA) is applied to extract the image block-global features. Unlike typical image annotation methods which use local features or global features separately, we propose to extract global features from image local regions (block) with the expectation of: a) combining the advantages of local and global features; b) discovering multiple semantic meanings in one image. In the experiment, comparative studies have been done for the performance of block-global feature extraction methods with widely used local feature extraction method such as scale invariant feature transform. The results show that 2DPCA has a significantly better performance than the performance of other methods.
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paper_title: Modeling annotated data

paper_content:

We consider the problem of modeling annotated data---data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models which aim to describe such data, culminating in correspondence latent Dirichlet allocation, a latent variable model that is effective at modeling the joint distribution of both types and the conditional distribution of the annotation given the primary type. We conduct experiments on the Corel database of images and captions, assessing performance in terms of held-out likelihood, automatic annotation, and text-based image retrieval.
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paper_title: Region based image annotation through a training image based feature space

paper_content:

A large box of sufficient size to carry a coat is cut and folded with the use of a conventionally sized standard die from a single piece of sheet-form material and has side panel structures formed by having an exterior side panel, an integral hinge, and an interior side panel with latching tabs to interlock with the bottom of the box. The exterior and interior side panels have a reduced total height due to the hinged side panels being formed with a bias cut so as to reduce the effective height of the panels to be less than the total height of the completed box while still providing closure integrity and structural stability.
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paper_title: Object detection and localization using local and global features. Toward Category-Level Object Recognition

paper_content:

Traditional approaches to object detection only look at local pieces of the image, whether it be within a sliding window or the regions around an interest point detector. However, such local pieces can be ambiguous, especially when the object of interest is small, or imaging conditions are otherwise unfavorable. This ambiguity can be reduced by using global features of the image — which we call the “gist” of the scene — as an additional source of evidence. We show that by combining local and global features, we get significantly improved detection rates. In addition, since the gist is much cheaper to compute than most local detectors, we can potentially gain a large increase in speed as well.
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paper_title: Combining Local and Global Image Features for Object Class Recognition

paper_content:

Object recognition is a central problem in computer vision research. Most object recognition systems have taken one of two approaches, using either global or local features exclusively. This may be in part due to the difficulty of combining a single global feature vector with a set of local features in a suitable manner. In this paper, we show that combining local and global features is beneficial in an application where rough segmentations of objects are available. We present a method for classification with local features using non-parametric density estimation. Subsequently, we present two methods for combining local and global features. The first uses a "stacking" ensemble technique, and the second uses a hierarchical classification system. Results show the superior performance of these combined methods over the component classifiers, with a reduction of over 20% in the error rate on a challenging marine science application.
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paper_title: Learning the semantics of words and pictures

paper_content:

We present a statistical model for organizing image collections which integrates semantic information provided by associate text and visual information provided by image features. The model is very promising for information retrieval tasks such as database browsing and searching for images based on text and/or image features. Furthermore, since the model learns relationships between text and image features, it can be used for novel applications such as associating words with pictures, and unsupervised learning for object recognition.
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paper_title: Combining global , regional and contextual features for automatic image annotation

paper_content:

This paper presents a novel approach to automatic image annotation which combines global, regional, and contextual features by an extended cross-media relevance model. Unlike typical image annotation methods which use either global or regional features exclusively, as well as neglect the textual context information among the annotated words, the proposed approach incorporates the three kinds of information which are helpful to describe image semantics to annotate images by estimating their joint probability. Specifically, we describe the global features as a distribution vector of visual topics and model the textual context as a multinomial distribution. The global features provide the global distribution of visual topics over an image, while the textual context relaxes the assumption of mutual independence among annotated words which is commonly adopted in most existing methods. Both the global features and textual context are learned by a probability latent semantic analysis approach from the training data. The experiments over 5k Corel images have shown that combining these three kinds of information is beneficial in image annotation.
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paper_title: Image Classification and Querying using Composite Region Templates

paper_content:

The tremendous growth in digital imagery is driving the need for more sophisticated methods for automatic image analysis, cataloging, and searching. We present a method for classifying and querying images based on the spatial orderings of regions or objects using composite region templates (CRTs). The CRTs capture the spatial information statistically and provide a robust way to measure similarity in the presence of region insertions, deletions, substitutions, replications, and relocations. The CRTs can be used for classifying and annotating images by assigning symbols to the regions or objects and by extracting symbol strings from spatial scans of the images. The symbol strings can be decoded using a library of annotated CRTs to automatically label and classify the images. The CRTs can also be used for searching by sketch or example by measuring image similarity based on relative counts of the CRTs.
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paper_title: Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

paper_content:

We describe a model of object recognition as machine translation. In this model, recognition is a process of annotating image regions with words. Firstly, images are segmented into regions, which are classified into region types using a variety of features. A mapping between region types and keywords supplied with the images, is then learned, using a method based around EM. This process is analogous with learning a lexicon from an aligned bitext. For the implementation we describe, these words are nouns taken from a large vocabulary. On a large test set, the method can predict numerous words with high accuracy. Simple methods identify words that cannot be predicted well. We show how to cluster words that individually are difficult to predict into clusters that can be predicted well -- for example, we cannot predict the distinction between train and locomotive using the current set of features, but we can predict the underlying concept. The method is trained on a substantial collection of images. Extensive experimental results illustrate the strengths and weaknesses of the approach.
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paper_title: Automatic Image Annotation Using Global and Local Features

paper_content:

Automatic image annotation methods require a quality training image dataset, from which annotations for target images are obtained. At present, the main problem with these methods is their low effectiveness and scalability if a large-scale training dataset is used. Current methods use only global image features for search. We proposed a method to obtain annotations for target images, which is based on a novel combination of local and global features during search stage. We are able to ensure the robustness and generalization needed by complex queries and significantly eliminate irrelevant results. In our method, in analogy with text documents, the global features represent words extracted from paragraphs of a document with the highest frequency of occurrence and the local features represent key words extracted from the entire document. We are able to identify objects directly in target images and for each obtained annotation we estimate the probability of its relevance. During search, we retrieve similar images containing the correct keywords for a given target image. For example, we prioritize images where extracted objects of interest from the target images are dominant as it is more likely that words associated with the images describe the objects. We tailored our method to use large-scale image training datasets and evaluated it with the Corel5K corpus which consists of 5000 images from 50 Corel Stock Photo CDs.
---
paper_title: Fusion of Global and Local Features for Face Verification

paper_content:

A personalized feature combination scheme is proposed for face verification. ANFIS (adaptive neuro-fuzzy inference system) and SVM (support vector machine) are adopted respectively to form specialized feature representation for each subject by fusing global and local features. Instead of the common way for different subjects, we realize a new representation that adapts to each individual. Such adaptability in feature selection is inspired by face recognition in the HVS (human visual system) and results in an improved recognition rate.
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paper_title: Joint Equal Contribution of Global and Local Features for Image Annotation

paper_content:

Image annotation is a very important task as the number of photographs has gone sky-high. This paper describes our participation in the ImageCLEF Large Scale Visual Concept Detection and Annotation Task 2009. We present the method used for our best run. Our approach is inspired from a recently proposed method where joint equal contribution (JEC) of simple global color and texture features can outperform the state-of-the-art annotation techniques [10]. Our idea is that if such simple features could do so well, then the combination of higher-level features would do even better. Study has shown that the concurrent use of saliency and gist of the scene is a major trait of human vision system. Therefore, in this preliminary study, we propose to explore the combination of different visual features at global, local and scene levels including global and local color, texture, and gist of the scene. The experiments confirm that higher-level features lead to better performance. Through the experiments, we also found that using 40 nearest neighbors and HSV, HSV (at saliency regions), HAAR, GIST (full scene), GIST (scene at the center) as features produce the best result.We finally identify the weakness in our approach and ways on how the system could be optimized and improved.
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paper_title: A neural network model with adaptive structure for image annotation

paper_content:

A neural network model with adaptive structure for image annotation is proposed in this paper. The adaptive structure enables the proposed model to utilize both global and regional visual features, as well as correlative information of annotated keywords for annotation. In order to achieve an approximate global optimum rather than a local optimum, both genetic algorithm and traditional back-propagation algorithm, are combined for model training. The neural network model is experimented on a synthetic image dataset with controllable parameters, which has not been used in previous image annotation experiments. Experimental results demonstrate the effectiveness of the proposed model.
---
paper_title: Region based image annotation through multiple-instance learning

paper_content:

In an annotated image database, keywords are usually associated with images instead of individual regions, which poses a major challenge for any region based image annotation algorithm. In this paper, we propose to learn the correspondence between image regions and keywords through Multiple-Instance Learning (MIL). After a representative image region has been learned for a given keyword, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. The classification problem is then addressed using the Bayesian framework. The proposed image annotation method is evaluated on an image database with 5,000 images.
---
paper_title: Combining global , regional and contextual features for automatic image annotation

paper_content:

This paper presents a novel approach to automatic image annotation which combines global, regional, and contextual features by an extended cross-media relevance model. Unlike typical image annotation methods which use either global or regional features exclusively, as well as neglect the textual context information among the annotated words, the proposed approach incorporates the three kinds of information which are helpful to describe image semantics to annotate images by estimating their joint probability. Specifically, we describe the global features as a distribution vector of visual topics and model the textual context as a multinomial distribution. The global features provide the global distribution of visual topics over an image, while the textual context relaxes the assumption of mutual independence among annotated words which is commonly adopted in most existing methods. Both the global features and textual context are learned by a probability latent semantic analysis approach from the training data. The experiments over 5k Corel images have shown that combining these three kinds of information is beneficial in image annotation.
---
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: A neural network model with adaptive structure for image annotation

paper_content:

A neural network model with adaptive structure for image annotation is proposed in this paper. The adaptive structure enables the proposed model to utilize both global and regional visual features, as well as correlative information of annotated keywords for annotation. In order to achieve an approximate global optimum rather than a local optimum, both genetic algorithm and traditional back-propagation algorithm, are combined for model training. The neural network model is experimented on a synthetic image dataset with controllable parameters, which has not been used in previous image annotation experiments. Experimental results demonstrate the effectiveness of the proposed model.
---
paper_title: Combining Image-level and Segment-level Models for Automatic Annotation

paper_content:

For the task of assigning labels to an image to summarize its contents, many early attempts use segment-level information and try to determine which parts of the images correspond to which labels. Best performing methods use global image similarity and nearest neighbor techniques to transfer labels from training images to test images. However, global methods cannot localize the labels in the images, unlike segment-level methods. Also, they cannot take advantage of training images that are only locally similar to a test image. We propose several ways to combine recent image-level and segment-level techniques to predict both image and segment labels jointly. We cast our experimental study in an unified framework for both image-level and segment-level annotation tasks. On three challenging datasets, our joint prediction of image and segment labels outperforms either prediction alone on both tasks. This confirms that the two levels offer complementary information.
---
paper_title: Learning global and regional features for photo annotation

paper_content:

This paper describes a method that learns a variety of features to perform photo annotation. We introduce concept-specific regional features and combine them with global features. The regional features were extracted through a novel region selection algorithm based on Multiple Instance Learning. Supervised classification for photo annotation was learned using Support Vector Machines with extended Gaussian Kernels over the χ2 distance, together with a simple greedy feature selection. The method was evaluated using the ImageCLEF 2009 Photo Annotation task and competitive benchmarking results were achieved.
---
paper_title: Automatic Image Annotation Using Global and Local Features

paper_content:

Automatic image annotation methods require a quality training image dataset, from which annotations for target images are obtained. At present, the main problem with these methods is their low effectiveness and scalability if a large-scale training dataset is used. Current methods use only global image features for search. We proposed a method to obtain annotations for target images, which is based on a novel combination of local and global features during search stage. We are able to ensure the robustness and generalization needed by complex queries and significantly eliminate irrelevant results. In our method, in analogy with text documents, the global features represent words extracted from paragraphs of a document with the highest frequency of occurrence and the local features represent key words extracted from the entire document. We are able to identify objects directly in target images and for each obtained annotation we estimate the probability of its relevance. During search, we retrieve similar images containing the correct keywords for a given target image. For example, we prioritize images where extracted objects of interest from the target images are dominant as it is more likely that words associated with the images describe the objects. We tailored our method to use large-scale image training datasets and evaluated it with the Corel5K corpus which consists of 5000 images from 50 Corel Stock Photo CDs.
---
paper_title: A neural network model with adaptive structure for image annotation

paper_content:

A neural network model with adaptive structure for image annotation is proposed in this paper. The adaptive structure enables the proposed model to utilize both global and regional visual features, as well as correlative information of annotated keywords for annotation. In order to achieve an approximate global optimum rather than a local optimum, both genetic algorithm and traditional back-propagation algorithm, are combined for model training. The neural network model is experimented on a synthetic image dataset with controllable parameters, which has not been used in previous image annotation experiments. Experimental results demonstrate the effectiveness of the proposed model.
---
paper_title: Combining Image-level and Segment-level Models for Automatic Annotation

paper_content:

For the task of assigning labels to an image to summarize its contents, many early attempts use segment-level information and try to determine which parts of the images correspond to which labels. Best performing methods use global image similarity and nearest neighbor techniques to transfer labels from training images to test images. However, global methods cannot localize the labels in the images, unlike segment-level methods. Also, they cannot take advantage of training images that are only locally similar to a test image. We propose several ways to combine recent image-level and segment-level techniques to predict both image and segment labels jointly. We cast our experimental study in an unified framework for both image-level and segment-level annotation tasks. On three challenging datasets, our joint prediction of image and segment labels outperforms either prediction alone on both tasks. This confirms that the two levels offer complementary information.
---
paper_title: A study of block-global feature based supervised image annotation

paper_content:

In order to get better semantic annotation performance, block-global features are extracted as low-level visual features for image semantic annotation. Specifically, wellknown global feature extraction method, namely two-dimensional principal component analysis (2DPCA) is applied to extract the image block-global features. Unlike typical image annotation methods which use local features or global features separately, we propose to extract global features from image local regions (block) with the expectation of: a) combining the advantages of local and global features; b) discovering multiple semantic meanings in one image. In the experiment, comparative studies have been done for the performance of block-global feature extraction methods with widely used local feature extraction method such as scale invariant feature transform. The results show that 2DPCA has a significantly better performance than the performance of other methods.
---
paper_title: Combining global , regional and contextual features for automatic image annotation

paper_content:

This paper presents a novel approach to automatic image annotation which combines global, regional, and contextual features by an extended cross-media relevance model. Unlike typical image annotation methods which use either global or regional features exclusively, as well as neglect the textual context information among the annotated words, the proposed approach incorporates the three kinds of information which are helpful to describe image semantics to annotate images by estimating their joint probability. Specifically, we describe the global features as a distribution vector of visual topics and model the textual context as a multinomial distribution. The global features provide the global distribution of visual topics over an image, while the textual context relaxes the assumption of mutual independence among annotated words which is commonly adopted in most existing methods. Both the global features and textual context are learned by a probability latent semantic analysis approach from the training data. The experiments over 5k Corel images have shown that combining these three kinds of information is beneficial in image annotation.
---

Title: Hybrid Image Representation Methods for Automatic Image Annotation: A Survey

Section 1: INTRODUCTION
Description 1: This section introduces the background, importance, and classification of image retrieval methods, with a particular focus on automatic image annotation. It also outlines the structure and aim of the paper.

Section 2: IMAGE REPRESENTATION
Description 2: This section describes the process of image representation, highlighting different types of features used, including general features and domain-specific features.

Section 3: GLOBAL-FEATURE-BASED IMAGE ANNOTATION
Description 3: This section reviews various image annotation approaches that utilize global features, explaining how these features are extracted and used in different models for image annotation.

Section 4: LOCAL-FEATURE-BASED IMAGE ANNOTATION
Description 4: This section discusses image annotation methods that are based on local features, distinguishing between block-based and region-based techniques.

Section 5: HYBRID METHODS
Description 5: This section explores emerging image annotation methods that combine both global and local features, explaining the advantages and approaches for hybrid image representation.

Section 6: DISCUSSION AND CONCLUSIONS
Description 6: This section summarizes the findings from the survey, highlighting key points, discussing limitations of current methods, and suggesting potential future research directions.