Modeling and Classification of MPEG VBR Video Data using Gradient-based Fuzzy c_means with Divergence Measure 


Vol. 29,  No. 7, pp. 931-936, Jul.  2004


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  Abstract

GBFCM(DM), Gradient-based Fuzzy c-means with Divergence Measure, for efficient clustering of GPDF(Gaussian Probability Density Function) in MPEG VBR video data modeling is proposed in this paper. The proposed GBFCM(DM) is based on GBFCM( Gradient-based Fuzzy c-means) with the Divergence for its distance measure. In this paper, sets of real-time MPEG VBR Video traffic data are considered. Each of 12 frames MPEG VBR Video data are first transformed to l2-dimensional data for modeling and the transformed 12-dimensional data are pass through the proposed GBFCM(DM) for classification. The GBFCM(DM) is compared with conventional FCM and GBFCM algorithms. The results show that the GBFCM(DM) gives 5~15% improvement in False Alann Rate over conventional algorithms such as FCM and GBFCM.

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  Cite this article

[IEEE Style]

D. Park and B. Kim, "Modeling and Classification of MPEG VBR Video Data using Gradient-based Fuzzy c_means with Divergence Measure," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 7, pp. 931-936, 2004. DOI: .

[ACM Style]

Dong-Chul Park and Bong-Joo Kim. 2004. Modeling and Classification of MPEG VBR Video Data using Gradient-based Fuzzy c_means with Divergence Measure. The Journal of Korean Institute of Communications and Information Sciences, 29, 7, (2004), 931-936. DOI: .

[KICS Style]

Dong-Chul Park and Bong-Joo Kim, "Modeling and Classification of MPEG VBR Video Data using Gradient-based Fuzzy c_means with Divergence Measure," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 7, pp. 931-936, 7. 2004.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880