Image Indexing and Retrieval Using Object-based Point Feature Maps

Abstract

Multimedia data such as audios, images, and videos are semantically richer than standard alphanumeric data. Because of the nature of images as combinations 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 prototype implementation particularly explores image indexing and retrieval using 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-level semantics into an image query. This is accomplished through the inclusion of the spatial distribution of point-based image object features, the spatial distribution of the image objects themselves, and image object class identifiers. We introduce a generic image model, give our ideas on how to represent the low- and high-level semantics of an image object, discuss our notion of image object similarity, and define four types of image queries supported by the OBIR system. We also propose an application of our approach to neurological surgery training.