Statistical summarization of content features for fast near-duplicate video detection

  • Authors:

  • Heng Tao Shen;Xiaofang Zhou;Zi Huang;Jie Shao

  • Affiliations:

  • The University of Queensland, Brisbane, Australia;The University of Queensland, Brisbane, Australia;The Open University, Milton Keynes, United Kingdom;The University of Queensland, Brisbane, Australia

  • Venue:
  • Proceedings of the 15th international conference on Multimedia
  • Year:
  • 2007

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Abstract

This paper outlines a system for detecting near-duplicate videos based on a novel summarization of content features for each clip. It captures the dominating content and content changing trends of a video, so this representation is very compact and effective. Unlike traditional frame-to-frame comparisons that involve quadratic computational complexity, the similarity measure of our method is only linear in dimensionality of feature space and independent of video length. To further improve the search efficiency for very large video databases, an effective indexing structure is deployed to significantly reduce the number of videos for comparison. This demo shows that our system can accurately find near-duplicates from a collection of tens of thousands of video clips extremely fast.