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)


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.


False Alarm Rate Covariance Information Code Vector Winner Neuron Gaussian Probability Density Function 
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  1. 1.
    Dawood, A.M., Ghanbari, M.: MPEG video modeling based on scene description. In: IEEE Int. Conf. Image Processing, Chicago, IL, vol. 2, pp. 351–355 (1998)Google Scholar
  2. 2.
    Patel, N., Sethi, I.K.: Video shot detection and characterization for video databases. Pattern Recog. 30, 583–592 (1977)CrossRefGoogle Scholar
  3. 3.
    Liang, Q., Mendel, J.M.: MPEG VBR Video Traffic Modeling and Classification Using Fuzzy Technique. IEEE Trans. Fuzzy Systems 9, 183–193 (2001)CrossRefGoogle Scholar
  4. 4.
    Dimitrova, N., Golshani, F.: Motion recovery for video content classification. ACM Trans. Inform. Sust. 13, 408–439 (1995)CrossRefGoogle Scholar
  5. 5.
    Manzoni, P., Cremonesi, P., Serazzi, G.: Workload models of VBR video traffic and their use in resource allocation policies. IEEE Trans. Networking 7, 387–397 (1999)CrossRefGoogle Scholar
  6. 6.
    Krunz, M., Sass, R., Hughes, H.: Statistical characteristics and multiplexing of MPEG streams. In: Proc. IEEE Int. Conf. Comput. Commun., INFOCOM 1995, Boston, MA, pp. 445–462 (1995)Google Scholar
  7. 7.
    Rose, O.: Satistical properties of MPEG video traffic and their impact on traffic modeling in ATM systems, Univ. Wurzburg,Inst. Comput. Sci., Rep., 101 (1995)Google Scholar
  8. 8.
    Bezdek, J.C.: A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Mach. Int. 2, 1–8 (1980)zbMATHCrossRefGoogle Scholar
  9. 9.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, New York (1981)zbMATHGoogle Scholar
  10. 10.
    Park, D.C., Dagher, I.: Gradient Based Fuzzy c-means (GBFCM) Algorithm. In: IEEE Int. Conf. on Neural Networks, ICNN 1994, vol. 3, pp. 1626–1631 (1994)Google Scholar
  11. 11.
    Looney, C.: Pattern Recognition Using Neural Networks, pp. 252–254. Oxford University press, New York (1997)Google Scholar
  12. 12.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum, New York (1981)zbMATHGoogle Scholar
  13. 13.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press Inc., London (1990)Google Scholar
  14. 14.
    Park, D.C.: Centroid Neural Network for Unsupervised Competitive Learning. IEEE Tr. on Neural Networks 11, 520–528 (2000)CrossRefGoogle Scholar
  15. 15.
    Park, D.C., Woo, Y.J.: Weighted centroid neural network for edge reserving image compression. IEEE Tr. on Neural Networks 12, 1134–1146 (2001)CrossRefGoogle Scholar

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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