Relevance feedback using a Bayesian classifier in content-based image retrieval

Zhong Su; HongJiang Zhang; Shao-peng Ma

doi:10.1117/12.410918

1 January 2001 Relevance feedback using a Bayesian classifier in content-based image retrieval

Zhong Su, HongJiang Zhang, Shao-peng Ma

Author Affiliations +

Proceedings Volume 4315, Storage and Retrieval for Media Databases 2001; (2001) https://doi.org/10.1117/12.410918
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

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.

Citation Download Citation

Zhong Su, HongJiang Zhang, and Shao-peng Ma "Relevance feedback using a Bayesian classifier in content-based image retrieval", Proc. SPIE 4315, Storage and Retrieval for Media Databases 2001, (1 January 2001); https://doi.org/10.1117/12.410918

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