Content-based image retrieval

About: Content-based image retrieval is a research topic. Over the lifetime, 6916 publications have been published within this topic receiving 150696 citations. The topic is also known as: CBIR.

Evaluation of Texture Features for Content-Based Image Retrieval

Abstract: We have carried out a detailed evaluation of the use of texture features in a query-by-example approach to image retrieval. We used 3 radically different texture feature types motivated by i) statistical, ii) psychological and iii) signal processing points of view. The features were evaluated and tested on retrieval tasks from the Corel and TRECVID2003 image collections. For the latter we also looked at the effects of combining texture features with a colour feature.

A Review on Image Feature Extraction and Representation Techniques

Abstract: Feature extraction and representation is a crucial step for multimedia processing. How to extract ideal features that can reflect the intrinsic content of the images as complete as possible is still a challenging problem in computer vision. However, very little research has paid attention to this problem in the last decades. So in this paper, we focus our review on the latest development in image feature extraction and provide a comprehensive survey on image feature representation techniques. In particular, we analyze the effectiveness of the fusion of global and local features in automatic image annotation and content based image retrieval community, including some classic models and their illustrations in the literature. Finally, we summarize this paper with some important conclusions and point out the future potential research directions.

Content-based hierarchical classification of vacation images

Abstract: 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 of interest. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified into indoor/outdoor classes, outdoor images are further classified into city/landscape classes, and finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a vector quantizer can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. On a database of 6931 vacation photographs, our system achieved an accuracy of 90.5% for indoor vs. outdoor classification, 95.3% for city vs. landscape classification, 96.6% for sunset vs. forest and mountain classification, and 95.5% for forest vs. mountain classification. We further develop a learning paradigm to incrementally train the classifiers as additional training samples become available and also show preliminary results for feature size reduction using clustering techniques.

Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR)

Abstract: An implementation of a technology, described herein, for relevance-feedback, content-based facilitating accurate and efficient image retrieval minimizes the number of iterations for user feedback regarding the semantic relevance of exemplary images while maximizing the resulting relevance of each iteration. One technique for accomplishing this is to use a Bayesian classifier to treat positive and negative feedback examples with different strategies. In addition, query refinement techniques are applied to pinpoint the users' intended queries with respect to their feedbacks. These techniques further enhance the accuracy and usability of relevance feedback. This abstract itself is not intended to limit the scope of this patent. The scope of the present invention is pointed out in the appending claims.

Semi-supervised distance metric learning for Collaborative Image Retrieval

Abstract: Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called ldquoCollaborative Image Retrievalrdquo (CIR). To effectively explore the log data, we propose a novel semi-supervised distance metric learning technique, called ldquoLaplacian Regularized Metric Learningrdquo (LRML), for learning robust distance metrics for CIR. Different from previous methods, the proposed LRML method integrates both log data and unlabeled data information through an effective graph regularization framework. We show that reliable metrics can be learned from real log data even they may be noisy and limited at the beginning stage of a CIR system. We conducted extensive evaluation to compare the proposed method with a large number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information.