Video copy detection using multiple visual cues and MPEG-7 descriptors

  • Authors:

  • Onur Küçüktunç;Muhammet Baştan;Uğur Güdükbay;Özgür Ulusoy

  • Affiliations:

  • Department of Computer Science and Engineering, The Ohio State University, 43210 OH, United States;Department of Computer Engineering, Bilkent University, 06800 Ankara, Turkey;Department of Computer Engineering, Bilkent University, 06800 Ankara, Turkey;Department of Computer Engineering, Bilkent University, 06800 Ankara, Turkey

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2010

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Abstract

We propose a video copy detection framework that detects copy segments by fusing the results of three different techniques: facial shot matching, activity subsequence matching, and non-facial shot matching using low-level features. In facial shot matching part, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In activity subsequence matching part, a spatio-temporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Lastly, the non-facial shots are matched using low-level MPEG-7 descriptors and dynamic-weighted feature similarity calculation. The proposed framework is tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results are compared with the results of top-8 most successful techniques submitted to this task. Promising results are obtained in terms of both effectiveness and efficiency.