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A Novel Feature Weighted Clustering Algorithm Based on Rough Sets for Shot Boundary Detection

  • Bing Han
  • Xinbo Gao
  • Hongbing Ji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)

Abstract

Shot boundary detection as the crucial step attracts much more research interests in recent years. To partition news video into shots, many metrics were constructed to measure the similarity among video frames based on all the available video features. However, too many features will reduce the efficiency of the shot boundary detection. Therefore, it is necessary to perform feature reduction before shot boundary detection. For this purpose, the classification method based on clustering algorithm of Variable Precision Rough-Fuzzy Sets and Variable Precision Rough Sets for feature reduction and feature weighting is proposed. According to the particularity of news scenes, shot transition can be divided into three types: cut transition, gradual transition and no transition. The efficiency of the proposed method is extensively tested on UCI data sets and more than 3 h of news programs and 96.2% recall with 96.3% precision have been achieved.

Keywords

Categorical Feature Mixed Data Shot Boundary Conditional Attribute Shot Boundary Detection 
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 2006

Authors and Affiliations

  • Bing Han
    • 1
  • Xinbo Gao
    • 1
  • Hongbing Ji
    • 1
  1. 1.School of Electronic EngineeringXidian Univ.Xi’anChina

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