Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A Bayesian Method for Content-Based Image Retrieval by Use of Relevance Feedback
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Pattern Recognition Methods for Querying and Browsing Technical Documentation
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A few steps towards on-the-fly symbol recognition with relevance feedback
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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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.