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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.