Image indexing and retrieval using object-based point feature maps

Multimedia data such as audios, images, and videos are semantically richer than standard alphanumeric data. Because of the nature of images as combina tions of objects, content-based image retrieval should allow users to query by image objects with finer granularity than a whole image. In this paper, we address a web-based object-based image retrieval (OBIR) system. Its pro totype implementation particularly explores image indexing and retrieval us ing object-based point feature maps. An important contribution of this work is its ability to allow a user to easily incorporate both low- and high-le vel semantics into an image query. This is accomplished through the inclusi on of the spatial distribution of point-based image object features, the sp atial distribution of the image objects themselves, and image object class identifiers. We introduce a generic image model, give our ideas on how to r epresent the low- and high-level semantics of an image object, discuss our notion of image object similarity, and define four types of image queries s upported by the OBIR system. We also propose an application of our approach to neurological surgery training. (C) 2000 Academic Press.