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Proceedings Paper

Relevance feedback using a Bayesian classifier in content-based image retrieval
Author(s): Zhong Su; HongJiang Zhang; Shao-peng Ma
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Paper Abstract

As an effective solution of the content-based image retrieval problems, relevance feedback has been put on many efforts for the past few years. In this paper, we propose a new relevance feedback approach with progressive leaning capability. It is based on a Bayesian classifier and treats positive and negative feedback examples with different strategies. It can utilitize previous users' feedback information to help the current query. Experimental results show that our algorithm achieves high accuracy and effectiveness on real-world image collections.

Paper Details

Date Published: 1 January 2001
PDF: 10 pages
Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); doi: 10.1117/12.410918
Show Author Affiliations
Zhong Su, Tsinghua Univ. (China)
HongJiang Zhang, Microsoft Research China (China)
Shao-peng Ma, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 4315:
Storage and Retrieval for Media Databases 2001
Minerva M. Yeung; Chung-Sheng Li; Rainer W. Lienhart, Editor(s)

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