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HISA: a query system bridging the semantic gap for large image databases

We propose a novel system called HISA for organizing very large image databases. HISA implements the first known data structure to capture both the ontological knowledge and visual features for effective and effcient retrieval of images by either keywords, image examples, or both. HISA employs automatic image annotation technique, ontology analysis and statistical analysis of domain knowledge to precompute the data structure. Using these techniques, HISA is able to bridge the gap between the image semantics and the visual features, therefore providing more user-friendly and high-performance queries. We demonstrate the novel data structure employed by HISA, the query algorithms, and the pre-computation process.