小波域基于分段Hurst指数的视频流分类

Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain

现有的视频流分类方法体现出内容依赖及特征依赖的局限性,该文引入流量分形理论,并在小波域内,提出一种基于Hurst指数的Fractals分类模型以改进不足。为此,该文首先描述流的分形性质,定义流的Hurst指数,推导小波域内Hurst指数的估计过程。然后,基于代价函数优化分段目标,用聚类差异度方法计算分段Hurst指数的总体差异量,再基于最大类间方差阈值进行分析,从而实现视频流的细粒度分类。研究结果表明,该文提出的分类方法,以随机数据的变化特性为内容,突破了内容依赖的局限性,解决了特征制约的瓶颈,提高了视频流的分类效果。

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.