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Title: Feature filtering in relevance feedback of image retrieval based on a statistical approach
Authors: Fu, H
Chi, ZG 
Feng, DD
Keywords: Content-based retrieval
Feature extraction
Filtering theory
Image retrieval
Image sampling
Relevance feedback
Statistical analysis
Issue Date: 2004
Publisher: IEEE
Source: ISIMP 2004 : proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing : October 20-22, 2004, Hong Kong, p. 647-650 How to cite?
Abstract: Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since 90's. In this paper, a feature filter whose parameters are computed by a statistical re-sampling approach is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experimental results show that the proposed approach is more efficient and robust than the traditional method.
ISBN: 0-7803-8687-6
DOI: 10.1109/ISIMP.2004.1434147
Rights: © 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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