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小波域基于分段Hurst指數(shù)的視頻流分類

湯萍萍,  董育寧

湯萍萍, 董育寧. 小波域基于分段Hurst指數(shù)的視頻流分類[J]. 電子與信息學報, 2017, 39(6): 1298-1304. doi: 10.11999/JEIT160745
引用本文: 湯萍萍, 董育寧. 小波域基于分段Hurst指數(shù)的視頻流分類[J]. 電子與信息學報, 2017, 39(6): 1298-1304. doi: 10.11999/JEIT160745
TANG Pingping, DONG Yuning. Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1298-1304. doi: 10.11999/JEIT160745
Citation: TANG Pingping, DONG Yuning. Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain[J]. Journal of Electronics & Information Technology, 2017, 39(6): 1298-1304. doi: 10.11999/JEIT160745

小波域基于分段Hurst指數(shù)的視頻流分類

doi: 10.11999/JEIT160745
  • 2.

    (南京郵電大學通信與信息工程學院 南京 210003) ②(安徽師范大學物理與電子信息學院 蕪湖 241000)

基金項目: 

國家自然科學基金(61271233, 60972038, 61401004)，華為HIRP創(chuàng)新項目

Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain

  • 2.

    (College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China)

Funds: 

The National Natural Science Foundation of China (61271233, 60972038, 61401004), Huawei Innovation Research Program (HIRP)

  • 摘要: 現(xiàn)有的視頻流分類方法體現(xiàn)出內(nèi)容依賴及特征依賴的局限性，該文引入流量分形理論，并在小波域內(nèi)，提出一種基于Hurst指數(shù)的Fractals分類模型以改進不足。為此，該文首先描述流的分形性質，定義流的Hurst指數(shù)，推導小波域內(nèi)Hurst指數(shù)的估計過程。然后，基于代價函數(shù)優(yōu)化分段目標，用聚類差異度方法計算分段Hurst指數(shù)的總體差異量，再基于最大類間方差閾值進行分析，從而實現(xiàn)視頻流的細粒度分類。研究結果表明，該文提出的分類方法，以隨機數(shù)據(jù)的變化特性為內(nèi)容，突破了內(nèi)容依賴的局限性，解決了特征制約的瓶頸，提高了視頻流的分類效果。
    Abstract: 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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出版歷程
  • 收稿日期:  2016-07-14
  • 修回日期:  2017-03-01
  • 刊出日期:  2017-06-19

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