ÐRetrieving images from large and varied collections using image content as a key is a challenging and important problem. We present a new image representation that provides a transformation from the raw pixel data to a small set of image regions that are coherent in color and texture. This ªBlobworldº representation is created by clustering pixels in a joint color-texture-position feature space. The segmentation algorithm is fully automatic and has been run on a collection of 10,000 natural images. We describe a system that uses the Blobworld representation to retrieve images from this collection. An important aspect of the system is that the user is allowed to view the internal representation of the submitted image and the query results. Similar systems do not offer the user this view into the workings of the system; consequently, query results from these systems can be inexplicable, despite the availability of knobs for adjusting the similarity metrics. By finding image regions that roughly correspond to objects, we allow querying at the level of objects rather than global image properties. We present results indicating that querying for images using Blobworld produces higher precision than does querying using color and texture histograms of the entire image in cases where the image contains distinctive objects.

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Retrieving images from large and varied collections using image content as a key is

Retrieving images from very large collections using image content as a key is becoming an important problem. Users prefer to ask for pictures using notions of content that are strongly oriented to the presence of objects, which are quite abstractly defined. Computer programs that implement these queries automatically are desirable but are hard to build because conventional object recognition techniques from computer vision cannot recognize very general objects in very general contexts. This paper describes an approach to object recognition structured around a sequence of increasingly specialized grouping activities that assemble coherent regions of image that can be shown to satisfy increasingly stringent constraints. The constraints that are satisfied provide a form of object classification in quite general contexts. This view of recognition is distinguished by far richer involvement of early visual primitives, including color and texture; the ability to deal with rather general objects in uncontrolled configurations and contexts; and a satisfactory notion of classification. These properties are illustrated with three case studies: one demonstrates the use of descriptions that fuse color and spatial properties; one shows how trees can be described by fusing texture and geometric properties; and one shows how this view of recognition yields a program that can tell, quite accurately, whether a picture contains naked people or not.