Jing Peng, Bir Bhanu and Shan Qing,
Probabilistic Feature Relevance Learning for Content-Based
Image Retrieval,
Computer Vision and Image Understanding (special issue on content-based access for image
and video libraries),
75, 1/2, pp. 150-164, July/August 1999.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 fixed (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 efficacy of our technique using both simulated and real-world data.