Hybrid video information system supporting content-based retrieval, similarity retrieval and query reformulation

We suggest a hybrid video information system (HVIS) which provides the content-based query integrating feature-based queries and annotation-based queries of video data similarity query. It supports the approximate query results by using the query reformulation in a case that the result of query does not exist. The HVIS suggests a three layered hybrid object-oriented metadata model (THOMM) to model metadata. The THOMM is composed of a raw-data layer for physical video stream, and a metadata layer to support the annotation-based retrieval, feature-based retrieval, and similarity retrieval and a semantic layer to reform the query. Based on this model, we suggest a video query language which deals the annotation-based queries, feature-based queries and the video query processor to process the query. Specially, in case similarity queries on given scene or object, we present the formula expressing degree of similarity based on the color spatial relation.