Quick identification of near-duplicate video sequences with cut signature

  • Authors:

  • Qing Xie;Zi Huang;Heng Tao Shen;Xiaofang Zhou;Chaoyi Pang

  • Affiliations:

  • School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072 and Queensland Research Laboratory, National ICT Australia, Sydney, Australia;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072;School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia 4072 and Queensland Research Laboratory, National ICT Australia, Sydney, Australia;The Australian e-Health Research Centre, Herston, Australia 4029

  • Venue:
  • World Wide Web
  • Year:
  • 2012

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Abstract

Online video stream data are surging to an unprecedented level. Massive video publishing and sharing impose heavy demands on continuous video near-duplicate detection for many novel video applications. This paper presents an accurate and accelerated system for video near-duplicate detection over continuous video streams. We propose to transform a high-dimensional video stream into a one-dimensional Video Trend Stream (VTS) to monitor the continuous luminance changes of consecutive frames, based on which video similarity is derived. In order to do fast comparison and effective early pruning, a compact auxiliary signature named CutSig is proposed to approximate the video structure. CutSig explores cut distribution feature of the video structure and contributes to filter candidates quickly. To scan along a video stream in a rapid way, shot cuts with local maximum AI (average information value) in a query video are used as reference cuts, and a skipping approach based on reference cut alignment is embedded for efficient acceleration. Extensive experimental results on detecting diverse near-duplicates in real video streams show the effectiveness and efficiency of our method.