12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00) Using Bayesian classifier in relevant feedback of image retrieval Vancouver, British Columbia, Canada November 13-November 15 ISBN: 0-7695-0909-6
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.
Index Terms:
image retrieval; content-based retrieval; relevance feedback; Bayes methods; image classification; relevance feedback; content-based image retrieval; Bayesian classifier; positive feedback examples; negative feedback examples; query space distribution; dibbling process; image penalization; retrieval refinement process; progressive learning capability; past feedback information; query refinement
Citation:
Zhong Su, Hongjiang Zhang, Shaoping Ma, "Using Bayesian classifier in relevant feedback of image retrieval," ictai, pp.0258, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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