A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Semisupervised SVM batch mode active learning with applications to image retrieval
ACM Transactions on Information Systems (TOIS)
Generalized discriminant analysis: a matrix exponential approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active reranking for web image search
IEEE Transactions on Image Processing
A blind watermarking scheme using new nontensor product wavelet filter banks
IEEE Transactions on Image Processing
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Relevance feedback (RF) has been wildely used to improve the performance of content-based image retrieval (CBIR). How to select a subset of features from a large-scale feature pool and to construct a suitable dissimilarity measure are key steps in RF. Biased discriminant analysis (BDA) has been proposed to select features during relevance feedback iterations. However, BDA assumes all positive feedbacks form a single Gaussian distribution which may not be the case for CBIR. Although kernel BDA can overcome the drawback to some extent, the kernel parameter tuning makes the online learning unfeasible. To avoid the parameter tuning problem and the single Gaussian distribution assumption in BDA, we construct a new nonparametric discriminant analysis (NDA). To address the small sample size problem in NDA, we introduce the regularization method and the null-space method. Because the regularization method may meet the ill-posed problem and the null-space method will lose somediscriminant information, we proposed here a full-space method. The proposedfull-space NDA is demonstrated to outperform BDA based RF significantly based on a large number of experiments in Corel database with 17,800 images.