|
|||||
|
|
| 无监督的视频业务特征分析与分类 | |
| 引用本文： | 姚利涛,董育宁. 无监督的视频业务特征分析与分类[J]. 应用科学学报, 2015, 33(2): 117-128. DOI: 10.3969/j.issn.0255-8297.2015.02.002 |
| 作者姓名： | 姚利涛 董育宁 |
| 作者单位： | 南京邮电大学通信与信息工程学院，南京210003 |
| 基金项目： | 国家自然科学基金(No.61271233,No.60972038);教育部博士点基金(No.20103223110001)资助 |
| 摘 要： | 基于机器学习的流统计特征识别的方法关键在于如何找到具有区分力度的业务流统 计特征. 为此，提出了一些能够较好地区分视频业务的QoS 相关的统计特征. 为了充分地发 挥多级聚类算法的优势，以灵活的特征选择策略标记不同层级的网络视频流，通过大量的真 实网络视频数据进行实验验证. 结果表明，该方法能比现有同类方法取得更高的分类准确率. |
| 关 键 词： | 视频流 统计特征 QoS 流分类 多级聚类 |
| 收稿时间： | 2014-07-17 |
| 修稿时间： | 2014-12-16 |
Unsupervised Feature Analysis and Classification of Video Streams |
|
| YAO Li-tao;DONG Yu-ning. Unsupervised Feature Analysis and Classification of Video Streams[J]. Journal of Applied Sciences, 2015, 33(2): 117-128. DOI: 10.3969/j.issn.0255-8297.2015.02.002 | |
| Authors: | YAO Li-tao DONG Yu-ning |
| Affiliation: | College of Telecommunications & Information Engineering, Nanjing University of Posts and;Telecommunications, Nanjing 210003, China |
| Abstract: | For recognition of flow statistical features based on machine learning, the key is to select distinguishable features of different traffic flows. This paper presents several QoSrelated statistical features that can well discriminate video traffics. To make full use of the advantages of hierarchical clustering algorithm, this paper uses a flexible feature selection strategy to mark the network video streaming of different levels. Experiments are performed on a large scale real network video data. The results show that the proposed method can achieve significantly higher classification accuracy compared to existing methods. Keywords: video streaming, statistical features, QoS, traffic classification, hierarchical clustering |
| Keywords: | |
| 本文献已被 CNKI 等数据库收录！ | |
| 点击此处可从《应用科学学报》浏览原始摘要信息 | |
| 点击此处可从《应用科学学报》下载全文 | |