UQLIPS: a real-time near-duplicate video clip detection system

  • Authors:

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

  • Affiliations:

  • The University of Queensland;The University of Queensland;The University of Queensland;The University of Queensland;The University of Queensland

  • Venue:
  • VLDB '07 Proceedings of the 33rd international conference on Very large data bases
  • Year:
  • 2007

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Abstract

Near-duplicate video clip (NDVC) detection is an important problem with a wide range of applications such as TV broadcast monitoring, video copyright enforcement, content-based video clustering and annotation, etc. For a large database with tens of thousands of video clips, each with thousands of frames, can NDVC search be performed in real-time? In addition to considering inter-frame similarity (i.e., spatial information), what is the impact of frame sequence similarity (i.e., temporal information) on search speed and accuracy? UQLIPS is a prototype system for online NDVC detection. The core of UQLIPS comprises two novel complementary schemes for detecting NDVCs. Bounded Coordinate System (BCS), a compact representation model ignoring temporal information, globally summarizes each video to a single vector which captures the dominating content and content changing trends of each clip. The other proposal, named FRAme Symbolization (FRAS), maps each clip to a sequence of symbols, and takes temporal order and sequence context information into consideration. Using a large collection of TV commercials, UQLIPS clearly demonstrates that it is feasible to perform real-time NDVC detection with high accuracy.