Scalable keyframe extraction using one-class support vector machine

Young Sik Choi, Sangyoun Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this paper, we present a scalable keyframe extraction method using one-class support vector machine. Keyframe extraction seeks to generate "good" images that best represent underlying video content and provide content-based access points. Criteria for "good" images play a major role for keyframe extraction process. Extracting "good images" can be viewed as detecting "novel images" among all the frames within a video. Therefore, keyframe extraction reduces to novelty detection problem. We describe how to efficiently solve the novelty detection problem using one-class support vector machine. We also present an algorithm of extracting keyframes in a scalable way so that one can access a video from coarse to fine resolution. We demonstrate the performance of our algorithm on several different types of videos.

Original languageEnglish
Pages (from-to)491-499
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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