Progressive search and retrieval in large image archives

In this paper, we describe the architecture and implementation of a framework to perform content-based search of an image database, where content is specified by the user at one or more of the following three abstraction levels: pixel, feature, and semantic. This framework incorporates a methodology that yields a computationally efficient implementation of image-processing algorithms, thus allowing the efficient extraction and manipulation of user-specified features and content during the execution of queries. The framework is well suited for searching scientific databases, such as satellite-image-, medical-, and seismic-data repositories, where the volume and diversity of the information do not allow the a priori generation of exhaustive indexes, but we have successfully demonstrated its usefulness on still-image archives.