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| Bayesian Probability Model Based on Region and Relevance Feedback |
| Zhao Yu-feng①; Zhao Yao①; Zhu Zhen-feng①② |
| ①Institute of information, Beijing Jiaotong University, Beijing 100044, China;②National key laboratory on machine perception, Peking University, Beijing 100871, China |
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Abstract Many researchers have found it can improve the retrieval performance by combining region-based representation and relevance feedback technology. Since the previous works have ignored the probabilistic distribution of regions in the same semantic class, it is hard to represent the semantic information effectively. In this paper, Bayesian probabilistic model based on region and relevance feedback is proposed. The probability model of image similarity can be constructed via the Bayesian classifier obtained by on-line learning and its certainty based on the least error probability of the nearest region in relevant images set. When it comes to the non-parameter density estimation technique for characterizing the region feature distribution, it also takes the collective distribution into consideration because of inaccurate segmentation. Thus, the posterior distribution of region feature can be estimated accurately, and the experimental results demonstrate its effectiveness.
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Received: 14 September 2006
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2008, Vol. 30 
