Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain

被引：0
|
作者：
Tang P. [1 ,2 ]
Dong Y. [1 ]
机构：
[1] College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing
[2] College of Physics and Electronic Information, Anhui Normal University, Wuhu
来源：
Dong, Yuning (dongyn@njupt.edu.cn) | 2017年 / Science Press卷 / 39期
基金：
中国国家自然科学基金;
关键词：
Classification; Fractals; Hurst exponent; Video flow; Wavelet;
D O I：
10.11999/JEIT160745
中图分类号：
学科分类号：
摘要：
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. © 2017, Science Press. All right reserved.
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页码：1298 / 1304
页数：6
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