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A Rough Set Approach to Video Genre Classification

  • Wengang Cheng
  • Chang’an Liu
  • Xingbo Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4179)

Abstract

Video classification provides an efficient way to manage and utilize the video data. Existing works on this topic fall into this category: enlarging the feature set until the classification is reliable enough. However, some features may be redundant or irrelevant. In this paper, we address the problem of choosing efficient feature set in video genre classification to achieve acceptable classification results but relieve computation burden significantly. A rough set approach is proposed. In comparison with existing works and the decision tree method, experimental results verify the efficiency of the proposed approach.

Keywords

Feature Selection Video Clip Gaussian Mixture Model Audio Feature Decision Tree Method 
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

  • Wengang Cheng
    • 1
  • Chang’an Liu
    • 1
  • Xingbo Wang
    • 1
  1. 1.Department of Computer ScienceNorth China Electric Power Univ.BeijingChina

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