Scalable Keyframe Extraction Using One-Class Support Vector Machine

  • YoungSik Choi 
  • Sangyoun Lee 
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2660)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • YoungSik Choi 
    • 1
  • Sangyoun Lee 
    • 2
  1. 1.Department of Computer EngineeringHankuk Aviation UniversityKyonggi-Do, Koyang-CityKorea
  2. 2.Service Development LaboratoryKorea TelecomSeoulKorea

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