Probabilistic Feature Relevance Learning for Content-Based Image Retrieval

Probabilistic Feature Relevance Learning for Content-Based Image Retrieval Jing Peng, Bir Bhanu, and Shan Qing Center for Research in Intelligent Systems University of California, Riverside, CA 92521 Email: fjp,bhanu,[email protected] Abstract Most of the current image retrieval systems use \one-shot" queries to a database to retrieve similar images. Typically a K-nearest neighbor kind of algorithm is used, where weights measuring feature importance along each input dimension remain xed (or manually tweaked by the user), in the computation of a given similarity metric. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space emanating from the query image. The manual adjustment of these weights is time consuming and exhausting. Moreover, it requires a very sophisticated user. In this paper, we present a novel probabilistic method that enables image retrieval procedures to automatically capture feature relevance based on user's feedback and that is highly adaptive to query locations. Experimental results are presented that demonstrate the e cacy of our technique using both simulated and real-world data.