Challenges and techniques for effective and efficient similarity search in large video databases

  • Authors:

  • Jie Shao;Heng Tao Shen;Xiaofang Zhou

  • Affiliations:

  • The University of Queensland, Brisbane QLD, Australia;The University of Queensland, Brisbane QLD, Australia;The University of Queensland, Brisbane QLD, Australia

  • Venue:
  • Proceedings of the VLDB Endowment
  • Year:
  • 2008

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Abstract

Searching relevant visual information based on content features in large databases is an interesting and changeling topic that has drawn lots of attention from both the research community and industry. This paper gives an overview of our investigations on effective and efficient video similarity search. We briefly introduce some novel techniques developed for two specific tasks studied in this PhD project: video retrieval in a large collection of segmented video clips, and video subsequence identification from a long unsegmented stream. The proposed methods for processing these two types of similarity queries have shown encouraging performance and are being incorporated into our prototype system of video search named UQLIPS, which has demonstrated some marketing potentials for commercialisation.