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![]() ![]() Salient regions are identified using region analysis, rank ordered and documented in a tree for further analysis. The arrangement of salient information into hierarchical tree structures in the spatial and temporal domains forms an important step to bridge the semantic salient gap. Salient analysis, on the other hand, is statistically based and highlights regions that are distinct from their surroundings, while also being scalable and repeatable. The recognition of semantic content in images is a problem that relies on prior knowledge and learnt information and that, to date, has only been partially solved. In recent years, such vast archives of video information have become available that human annotation of content is no longer feasible automation of video content analysis is therefore highly desirable. These tools populate the system's fact base and feature database to support both query types. Because of space limitations, we only discuss the Web-based visual query interface and its fact-extractor and video-annotator tools. To respond to user queries containing both spatio-temporal and semantic conditions, a query processor interacts with the knowledge base and object-relational database and integrates the results returned from these two system components. A feature database stored in an object-relational database management system handles semantic queries. The rules in the knowledge base significantly reduce the number of facts representing the spatio-temporal relations that the system needs to store. ![]() ![]() These queries contain any combination of conditions related to direction, topology, 3D relationships, object appearance, trajectory projection, and similarity-based object trajectories. A knowledge base, consisting of a fact base and a comprehensive rule set implemented in Prolog, handles spatio-temporal queries. The BilVideo video database management system provides integrated support for spatiotemporal and semantic queries for video.
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