Bayesian learning for image retrieval using multiple features



Publication Details

Wang, L. & Chan, K. Luk. (2000). Bayesian learning for image retrieval using multiple features. Intelligent Data Engineering and Automated Learning - IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents (pp. 473-478). Berlin: Springer-Verlag.


Image retrieval using multiple features often uses explicit weights that represent the importance of the features in their similarity metrics. In this paper, a novel retrieval method based on Bayesian Learning is presented. Instead of giving every feature a weight explicitly, the importance of a feature is regulated implicitly by learning a user's perception. Thus, the process of feature combination is adaptive and approximate to a user's perception. Experimental results demonstrate the signi cance of this method for improving the retrieval efficiency.

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