Scalable mining of large video databases using copy detection

  • Authors:
  • Sébastien Poullot;Michel Crucianu;Olivier Buisson

  • Affiliations:
  • Institut National de l'Audiovisuel and CEDRIC - CNAM, Bry-sur-Marne, France;CNAM, Paris, France;Institut National de l'Audiovisuel, Bry-sur-Marne, France

  • Venue:
  • MM '08 Proceedings of the 16th ACM international conference on Multimedia
  • Year:
  • 2008

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Abstract

Mining the video content itself can bring to light important information regarding the internal structure of large video databases, compensating for a lasting absence of extensive and reliable annotations. Many valuable links between video segments can be identified by content-based copy detection methods, where "copies" are transformed versions of original video sequences. To make this approach viable for large video databases, we put forward a new mining method relying on the definition of a compact keyframe-level descriptor and of a specific index structure. The performance obtained in detecting links between video segments is evaluated with the help of a ground truth and several illustrations are given. The scalability of the approach is then demonstrated for databases of up to 10,000 hours of video.