Feature filtering in relevance feedback of image retrieval based on a statistical approach

Hong FU, Zheru CHI, Dagan FENG

Research output: Chapter in Book/Report/Conference proceedingChapters

Abstract

Relevance feedback is a powerful tool to grasp the user's intention in image retrieval systems and has attracted many researchers' attention since the 1990s. A feature filter, whose parameters are computed by a statistical resampling 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. Copyright © 2004 by the Institute of Electrical and Electronics Engineers, Inc.
Original languageEnglish
Title of host publicationProceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech processing, ISIMP 2004
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages647-650
ISBN (Print)0780386876
DOIs
Publication statusPublished - 2004

Citation

Fu, H., Chi, Z., & Feng, D. (2004). Feature filtering in relevance feedback of image retrieval based on a statistical approach. In Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech processing, ISIMP 2004 (pp. 647-650). Piscataway, NJ: IEEE.

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