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Classification of MPEG Video Content Using Divergence Measure with Data Covariance

  • Dong-Chul Park
  • Chung-Nguyen Tran
  • Yunsik Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3767)

Abstract

This paper describes how the covariance information in MPEG video data can be incorporated into a distance measure and applies the resulting divergence measure to video content classification problems. The divergence measure is adopted into two different clustering algorithms, the Centroid Neural Network (CNN) and the Gradient Based Fuzzy c-Means (GBFCM) for MPEG video data classification problems, movie or sports. Experiments on 16 MPEG video traces show that the divergence measure with covariance information can decrease the False Alarm Rate (FAR) in classification as much as 46.6% on average.

Keywords

False Alarm Rate Covariance Information Code Vector Winner Neuron Gaussian Probability Density Function 
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 2005

Authors and Affiliations

  • Dong-Chul Park
    • 1
  • Chung-Nguyen Tran
    • 1
  • Yunsik Lee
    • 2
  1. 1.ICRL, Dept. of Information EngineeringMyong Ji UniversityKorea
  2. 2.SoC Research CenterKorea Electronics Tech. Inst.SeongnamKorea

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