Bayesian learning for image retrieval using multiple features
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 signicance of this method for improving the retrieval efficiency.
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.