Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain
TANG Pingping①② DONG Yuning①
①(College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China) ②(College of Physics and Electronic Information, Anhui Normal University, Wuhu 241000, China)
The existing methods about fine classification of video traffic suffer from a couple of serious limitations: content dependency and feature dependency. Then, theory of fractals is introduced in this paper, and in wavelet domain, a classification model named Fractals is presented based on Hurst exponent. For this purpose, fractal properties of video flows are described, the corresponding Hurst exponent is defined, and the estimated value of Hurst exponent in wavelet domain is derived. Then, the optimum segments based on cost function is analyzed, the statistical differential level is calculated with the method of clustering, and the classification results are deduced with maximum between-cluster variance threshold. The result shows that the classification method with Fractals, which takes data variability as the content, makes up for the defect of content dependency and feature dependency, and demonstrates wonderful performance when classifying video flows.
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