Recognition of Images in Large Databases Using a Learning Framework
Serge Belongie, Chad Carson, Hayit Greenspan, Jitendra Malik · 1997
Retrieving images from very large collections using image content as a key is becoming an important problem. Classifying images into visual categories and finding objects in image databases are two major challenges in the field. This paper describes our approach toward the first of the two tasks, the generalization of which we believe will assist in the second task as well. We define a blobworld representation which provides a transition from the raw pixel data to a small set of localized coherent regions in color and texture space. Learning is then utilized to extract a probabilistic interpretation of the scene. Experimental results are presented for more than 1000 images from the Corel photo collection. 1. Introduction Very large collections of images are becoming common, and users have a clear preference for accessing images in these databases based on their content---be it the general image category (e.g., animal scenes, landscapes, urban scenes) or particular objects that are pre...