Using Bayesian classifier in relevant feedback of image retrieval
نویسندگان
چکیده
Relevance feedback is a powerful technique in contentbased 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 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 the progressive learning capability that utilize past feedback information to help the current query. Experimental results show that our algorithm is effectiveness.
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