1 January 2001 Relevance feedback using a Bayesian classifier in content-based image retrieval
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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.
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Zhong Su, Zhong Su, HongJiang Zhang, HongJiang Zhang, Shao-peng Ma, 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); doi: 10.1117/12.410918; https://doi.org/10.1117/12.410918

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