International Journal of Computer Vision
OCRS: an interactive object-based image clustering and retrieval system
Multimedia Tools and Applications
An active feedback framework for image retrieval
Pattern Recognition Letters
BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval
Image and Vision Computing
A novel active learning approach for SVM based web image retrieval
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
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In this paper, the application of support vector machines (SVM) in relevance feedback for region-based image retrieval is investigated. Both the one class SVM as a class distribution estimator and two classes SVM as a classifier are taken into account. For the latter, two representative display strategies are studied. Since the common kernels often rely on inner product or L/sub p/ norm in the input space, they are infeasible in the region-based image retrieval systems that use variable-length representations. To resolve the issue, a new kind of kernel that is a generalization of Gaussian kernel is proposed. Experimental results on a database of 10,000 general-purpose images demonstrate the effectiveness and robustness of the proposed approach.