This research extends query language and user-interface technology to include context-based queries for image data. A context is a domain dependent body of knowledge which encodes the translation between, on one hand, user-level semantics and object classes and, on the other hand, database syntax and entities. A prototype image information system provides a testbed for the investigation of issues in image indexing, similarity measures, and knowledge-guided incremental querying. A set of image processing and feature extraction routines supplies the base for image indexing; this base is augmented with a data model that accounts for objects, their images, the image features, and events in the world involving the objects. A knowledge-base and associated user-interface support navigation within the data model to guide query formation and refinement. This research will develop new concepts in data modeling, image indexing, and user- interfaces, which will enable users to access the image data in such applications as global monitoring and scientific imaging, based on concepts, context, similarity, and image features.
