Abstract
Relevance feedback is a powerful technique in content-based image retrieval (CBIR) and has been an active research area for the past few years. In this paper, we propose a new relevance feedback approach based on a Bayesian classifier, and it treats positive and negative feedback examples with different strategies. For positive examples, a Bayesian classifier is used to determine the distribution of the query space. A 'dibbling' process is applied to penalize images that are near the negative examples in the query and retrieval refinement process. The proposed algorithm also has a progressive learning capability that utilizes past feedback information to help the current query. Experimental results show that our algorithm is effective.
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