A logical framework for visual information modeling and management

A unified semantic visual data-modeling framework is presented in the paper. In the proposed model, an extended conceptual graph is proposed as an annotation mechanism of a user's perceptual understanding of video objects, activities, and events. A precise definition of the term "domain knowledge" in visual information processing is presented. A conceptual structure, associated terms, visual feature extraction methods, and a set of constraints in feature extraction are considered as domain information. The proposed visual data model has six different abstraction layers. A higher level is more abstracted and more semantically summarized. A polygon-based bounding volume is used in video object approximation in space and time. We use a bounding volume in motion trajectory representation, rather than motion vectors. This model allows simultaneous access of both temporal and spatial information. The proposed model may be used as a referencing framework for various visual information management systems' developments.