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Classification of MPEG VBR Video Data Using Gradient-Based FCM with Divergence Measure

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3613))

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Abstract

An efficient approximation of the Gaussian Probability Density Function (GPDF) is proposed in this paper. The proposed algorithm, called the Gradient-Based FCM with Divergence Measure (GBFCM (DM)), employs the divergence measurement as its distance measure and utilizes the spatial characteristics of MPEG VBR video data for MPEG data classification problems. When compared with conventional clustering and classification algorithms such as the FCM and GBFCM, the proposed GBFCM(DM) successfully finds clusters and classifies the MPEG VBR data modelled by the 12-dimensional GPDFs.

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Author information

Authors and Affiliations

  1. Intelligent Computing Research Lab., Dept. of Information Engineering, Myong Ji University, Korea

    Dong-Chul Park

Authors
  1. Dong-Chul Park
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Park, DC. (2005). Classification of MPEG VBR Video Data Using Gradient-Based FCM with Divergence Measure. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_61

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  • DOI: https://doi.org/10.1007/11539506_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28312-6

  • Online ISBN: 978-3-540-31830-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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