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

As an effective solution of the content-based image retrieval (CBIR) 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 utilize previous users’ feedback information to help the current query. Experimental results show that our algorithm is achieves high accuracy and effectiveness on real-world image collections.