Hierarchical video content description and summarization using unified semantic and visual similarity

  • Authors:

  • Xingquan Zhu;Jianping Fan;Ahmed K. Elmagarmid;Xindong Wu

  • Affiliations:

  • Department of Computer Science, University of Vermont, Burlington, VT;Department of Computer Science, University of North Carolina, Charlotte, NC;Department of Computer Science, Purdue University, West Lafayette, IN;Department of Computer Science, University of Vermont, Burlington, VT

  • Venue:
  • Multimedia Systems
  • Year:
  • 2003

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Abstract

Video is increasingly the medium of choice for a variety of communication channels, resulting primarily from increased levels of networked multimedia systems. One way to keep our heads above the video sea is to provide summaries in a more tractable format. Many existing approaches are limited to exploring important low-level feature related units for summarization. Unfortunately, the semantics, content and structure of the video do not correspond to low-level features directly, even with closed-captions, scene detection, and audio signal processing. The drawbacks of existing methods are the following: (1) instead of unfolding semantics and structures within the video, low-level units usually address only the details, and (2) any important unit selection strategy based on low-level features cannot be applied to general videos. Providing users with an overview of the video content at various levels of summarization is essential for more efficient database retrieval and browsing. In this paper, we present a hierarchical video content description and summarization strategy supported by a novel joint semantic and visual similarity strategy. To describe the video content efficiently and accurately, a video content description ontology is adopted. Various video processing techniques are then utilized to construct a semi-automatic video annotation framework. By integrating acquired content description data, a hierarchical video content structure is constructed with group merging and clustering. Finally, a four layer video summary with different granularities is assembled to assist users in unfolding the video content in a progressive way. Experiments on real-word videos have validated the effectiveness of the proposed approach.