Model-Based Video Classification toward Hierarchical Representation, Indexing and Access

  • Authors:

  • Jianping Fan;Xingquan Zhu;Mohand-Said Hacid;Ahmed K. Elmagarmid

  • Affiliations:

  • Department of Computer Science, University of North Carolina at Charlotte, 9201 University City BLVD, Chartlotte, NC 28223, USA;Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA;LISI-INSA Lyon, 20 Avenue Albert Einstein, 69621 Villeurbanne, France;Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2002

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Abstract

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.