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Detecting Frequent Patterns in Video Using Partly Locality Sensitive Hashing

  • Koichi Ogawara
  • Yasufumi Tanabe
  • Ryo Kurazume
  • Tsutomu Hasegawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6468)

Abstract

Frequent patterns in video are useful clues to learn previously unknown events in an unsupervised way. This paper presents a novel method for detecting relatively long variable-length frequent patterns in video efficiently. The major contribution of the paper is that Partly Locality Sensitive Hashing (PLSH) is proposed as a sparse sampling method to detect frequent patterns faster than the conventional method with LSH. The proposed method was evaluated by detecting frequent everyday whole body motions in video.

Keywords

Hash Function Data Density Frequent Pattern Locality Sensitive Hashing Neighboring Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Koichi Ogawara
    • 1
  • Yasufumi Tanabe
    • 1
  • Ryo Kurazume
    • 1
  • Tsutomu Hasegawa
    • 1
  1. 1.Kyushu UniversityJapan

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