Model-based video classification toward hierarchical representation, indexing and access

In this paper, we develop a content-based video classification approach to support semantic categorization, high-dimensional indexing and multi-level access. Our contributions are in four points: (a) We first present a hierarchical video database model that captures the structures and semantics of video contents in databases. One advantage of this hierarchical video database model is that it can provide a framework for automatic mapping from high-level concepts to low-level representative features. (b) We second propose a set of useful techniques for exploiting the basic units (e.g., shots or objects) to access the videos in database. (c) We third suggest a learning-based semantic classification technique to exploit the structures and semantics of video contents in database. (d) We further develop a cluster-based indexing structure to both speed-up query-by-example and organize databases for supporting more effective browsing. The applications of this proposed multi-level video database representation and indexing structures for MPEG-7 are also discussed.