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Video Scene Segmentation Using Time Constraint Dominant-Set Clustering

  • Xianglin Zeng
  • Xiaoqin Zhang
  • Weiming Hu
  • Wanqing Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5916)

Abstract

Video scene segmentation plays an important role in video structure analysis. In this paper, we propose a time constraint dominant-set clustering algorithm for shot grouping and scene segmentation, in which the similarity between shots is based on autocorrelogram feature, motion feature and time constraint. Therefore, the visual evidence and time constraint contained in the video content are effectively incorporated into a unified clustering framework. Moreover, the number of clusters in our algorithm does not need to be predefined and thus it provides an automatic framework for scene segmentation. Compared with normalized cut clustering based scene segmentation, our algorithm can achieve more accurate results and requires less computing resources.

Keywords

Motion Feature Visual Evidence News Video Instructional Video Video Shot 
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 2010

Authors and Affiliations

  • Xianglin Zeng
    • 1
  • Xiaoqin Zhang
    • 1
  • Weiming Hu
    • 1
  • Wanqing Li
    • 2
  1. 1.National Laboratory of Pattern RecognitionInstitute of AutomationBeijingChina
  2. 2.SCSSEUniversity of WollongongAustralia

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