Hierarchical shot clustering for video summarization

Young Sik Choi, Sun Jeong Kim, Sangyoun Lee

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

6 Citations (Scopus)

Abstract

Digital video is rapidly becoming a communication medium for education, entertainment, and a variety of multimedia applications. With the size of the video collections growing to thousnads of hours, efficient searching, browsing, and managing video information have become of increasing importance. In this paper, we propose a novel hierarchical shot clustering method for video summarization which can efficiently generate a set of representative shots and provide a quick and efficient access to a large volume of video content. The proposed method is based on the compatibility measure that can represent correlations among shots in a video sequence. Experimental results on real life video sequences show that the resulting summary can retain the essential content of the original video.

Original languageEnglish
Title of host publicationComputational Science, ICCS 2002 - International Conference, Proceedings
Pages1100-1107
Number of pages8
EditionPART 3
Publication statusPublished - 2002 Dec 1
EventInternational Conference on Computational Science, ICCS 2002 - Amsterdam, Netherlands
Duration: 2002 Apr 212002 Apr 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume2331 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Computational Science, ICCS 2002
CountryNetherlands
CityAmsterdam
Period02/4/2102/4/24

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Choi, Y. S., Kim, S. J., & Lee, S. (2002). Hierarchical shot clustering for video summarization. In Computational Science, ICCS 2002 - International Conference, Proceedings (PART 3 ed., pp. 1100-1107). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2331 LNCS, No. PART 3).