Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain

TANG Pingping; DONG Yuning

doi:10.11999/JEIT160745

Volume 39 Issue 6

Jun. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(6): 1298-1304

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

Citation:

PDF( 282 KB)

Classifying Video Flows Based on Segmented Hurst Exponent in Wavelet Domain

doi: 10.11999/JEIT160745

TANG Pingping¹,
DONG Yuning²

1.
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)

Received Date: 2016-07-14
Rev Recd Date: 2017-03-01
Publish Date: 2017-06-19

Abstract

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.
- Video flow,
- Classification,
- Fractals,
- Hurst exponent,
- Wavelet

FullText(HTML)

References(22)

References

QAHHAR Muhammad Qadir. Mechanisms for QoE optimisation of video traffic: A review paper[J]. Analytical Biochemistry, 2015, 1(1): 40-42. doi: 10.17972/ajicta2015117.

KHAN N and MARTINI M G. Hysteresis based rate adaptation for scalable video traffic over an LTE downlink[C]. IEEE International Conference on Communication Workshop, London, UK, 2015: 1434-1439. doi: 10.1109/ICCW.2015. 7247380.

WANG Zaijian, DONG Yuning, SHI Haixian, et al. Internet video traffic classification using QoS features[C]. IEEE International Conference on Computing, Networking and Communications, Kauai, HI, USA, 2016: 1-5. doi: 10.1109/ ICCNC. 2016.7440599.

MOHD A B and NOR S B M. Towards a flow-based internet traffic classification for bandwidth optimization[J]. International Journal of Computer Science Security, 2014, 3(2): 146-153.

BARAKAT C, THIRAN P, IANNACCONE G, et al. Modeling internet backbone traffic at the flow level[J]. IEEE Transactions on Signal Processing Special Issue on Networking, 2003, 51(8): 2111-2124. doi: 10.1109/TSP.2003.814521.

LI W and YU X. An online flow-level packet classification method on multi-core network processor[C]. IEEE International Conference on Computational Intelligence and Security, Shenzhen, China 2015: 407-411. doi: 10.1109/CIS. 2015.104.

ZHANG D, ZHOU D, and JIN X. A content-adaptive video quality assessment method for online media service[J]. Multimedia Tools Applications, 2016: 1-21. doi: 10.1007/ s11042-016-3359-5.

SUN S B and CUI R Y. Player classification algorithm based on digraph in soccer video[C]. IEEE Conference on Information Technology and Artificial Intelligence, Chongqing, China, 2014: 459-463. doi: 10.1109/ITAIC.2014.7065092.

LIM J D, LEE C H, CHOI B C, et al. Implementation of automatic x-rated video classification and management system based on multimodal features[J]. International Journal of Advancements in Computing Technology, 2012, 4(23): 178-186. doi: 10.4156/ijact.vol4.issue23.21.

YU J, WANG M, and TAO D. Semisupervised multiview distance metric learning for cartoon synthesis[J]. IEEE Transactions on Image Processing, 2012, 21(11): 4636-4648. doi: 10.1109/TIP.2012.2207395.

NG Y H, HAUSKNECHT M, VIJAYANARASIMHAN S, et al. Beyond short snippets: Deep networks for video classification[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 4694-4702. doi: 10.1109/CVPR.2015.7299101.

JENNINGS C, PETERSON J, TERRIBERRY T, et al. Multiplexing of real-time transport protocol (RTP) traffic for browser based real-time communications (RTC)[J]. Communications and Informatics, 2011, 24 (6): 1-28.

ZENG C and CHEN J. Analysis and design based on DPI streaming media of traffic detection system[J]. Video Engineering, 2014, 36(5): 94-102.

KHEIR N, HAN X, and WOLLEY C. Behavioral fine-grained detection and classification of P2P bots[J]. Journal of Computer Virology Hacking Techniques, 2015, 11(4): 217-233. doi: 10.1007/s11416-014-0228-5.

WU Z, JIANG Y G, WANG J, et al. Exploring inter-feature and inter-class relationships with deep neural networks for video classification[C]. Proceedings of the ACM International Conference on Multimedia, Chengdu, China, 2014: 1-6. doi: 10.1109/ICMEW.2014.6890609.

KARPATHY A, TODERICI G, SHETTY S, et al. Large-scale video classification with convolutional neural Networks[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 1725-1732. doi: 10.1109/CVPR.2014.223.

NOSSENSON R and POLACHECK S. On-line flows classification of video streaming applications[C]. IEEE International Symposium on Network Computing and Applications, Cambridge, MA, USA, 2015: 251-258. doi: 10.1109/NCA.2015.51.

GANDHI V R, QU Y R, and PRASANNA V K. High-throughput hash-based online traffic classification engines on FPGA[C]. IEEE International Conference on Reconfigurable Computing and FPGAs, Cancun, Mexico, 2015: 1-6. doi: 10.1109/ReConFig.2014.7032530.

MANE A S and KAMDE P M. Video classification using SVM[J]. International Journal of Recent Technology Engineering, 2013, 2(3): 34-47.

LELAND W E, TAQQU M S, Willinger W, et al. On the self-similar nature of Ethernet traffic (extended version)[J]. IEEE/ACM Transactions on Networking, 1994, 2(1): 1-15. doi: 10.1109/90.282603.

HE H, WANG J, WEI H, et al. Fractal behavior of traffic volume on urban expressway through adaptive fractal analysis[J]. Physica A Statistical Mechanics Its Applications, 2015, 443(7): 518-525.

ALVAREZRAMIREZ J, IBARRAVALDEZ C, and RODRIGUEZ E. Fractal analysis of Jackson Pollock's painting evolution[J]. Chaos Solitons Fractals, 2016, 83(1): 97-104. doi: 10.1016/j.chaos.2015.11.034.

Relative Articles

Supplements(0)

Cited By

Proportional views