Detecting frequent patterns in video using partly locality sensitive hashing

Koichi Ogawara, Yasufumi Tanabe, Ryo Kurazume, Tsutomu Hasegawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers
Pages287-296
Number of pages10
EditionPART1
DOIs
Publication statusPublished - Sep 28 2011
EventInternational Workshops on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 9 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART1
Volume6468 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshops on Computer Vision, ACCV 2010
CountryNew Zealand
CityQueenstown
Period11/8/1011/9/10

Fingerprint

Frequent Pattern
Hashing
Locality
Sampling
Sampling Methods
Unknown
Motion

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ogawara, K., Tanabe, Y., Kurazume, R., & Hasegawa, T. (2011). Detecting frequent patterns in video using partly locality sensitive hashing. In Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers (PART1 ed., pp. 287-296). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6468 LNCS, No. PART1). https://doi.org/10.1007/978-3-642-22822-3_29

Detecting frequent patterns in video using partly locality sensitive hashing. / Ogawara, Koichi; Tanabe, Yasufumi; Kurazume, Ryo; Hasegawa, Tsutomu.

Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1. ed. 2011. p. 287-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6468 LNCS, No. PART1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ogawara, K, Tanabe, Y, Kurazume, R & Hasegawa, T 2011, Detecting frequent patterns in video using partly locality sensitive hashing. in Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART1, vol. 6468 LNCS, pp. 287-296, International Workshops on Computer Vision, ACCV 2010, Queenstown, New Zealand, 11/8/10. https://doi.org/10.1007/978-3-642-22822-3_29
Ogawara K, Tanabe Y, Kurazume R, Hasegawa T. Detecting frequent patterns in video using partly locality sensitive hashing. In Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1 ed. 2011. p. 287-296. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART1). https://doi.org/10.1007/978-3-642-22822-3_29
Ogawara, Koichi ; Tanabe, Yasufumi ; Kurazume, Ryo ; Hasegawa, Tsutomu. / Detecting frequent patterns in video using partly locality sensitive hashing. Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1. ed. 2011. pp. 287-296 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART1).
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