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World Wide Web

, Volume 15, Issue 3, pp 355–382 | Cite as

Quick identification of near-duplicate video sequences with cut signature

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

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.

Keywords

online video near-duplicate detection VTS CutSig shot cut alignment 

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Qing Xie
    • 1
  • Zi Huang
    • 1
    • 2
  • Heng Tao Shen
    • 1
  • Xiaofang Zhou
    • 1
    • 2
  • Chaoyi Pang
    • 3
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
  2. 2.Queensland Research LaboratoryNational ICT AustraliaSydneyAustralia
  3. 3.The Australian e-Health Research CentreHerstonAustralia

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