Abstract
Video-on-demand (VoD) services have become popular on the Internet in recent years. In VoD, it is challenging to support the VCR functionality, especially the jumps, while maintaining a smooth streaming quality. Previous studies propose to solve this problem by predicting the jump target locations and prefetching the contents. However, through our analysis on traces from a real-world VoD service, we find that it would be fundamentally difficult to improve a viewer’s VCR experience by simply predicting his future jumps, while ignoring the intentions behind these jumps.
Instead of the prediction-based approach, in this paper, we seek to support the VCR functionality by bookmarking the videos. There are two key techniques in our proposed methodology. First, we infer and differentiate viewers’ intentions in VCR jumps by decomposing the interseek times, using an expectation-maximization (EM) algorithm, and combine the decomposed inter-seek times with the VCR jumps to compute a numerical interest score for each video segment. Second, based on the interest scores, we propose an automated video bookmarking algorithm. The algorithm employs the time-series change detection techniques of CUSUMandMB-GT, and bookmarks videos by detecting the abrupt changes on their interest score sequences.We evaluate our proposed techniques using real-world VoD traces from dozens of videos. Experimental results suggest that with our methods, viewers’ interests within a video can be precisely extracted, and we can position bookmarks on the video’s highlight events accurately. Our proposed video bookmarking methodology does not require any knowledge on video type, contents, and semantics, and can be applied on various types of videos.
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References
Zheng C, Shen G, Li S. Distributed prefetching scheme for random seek support in peer-to-peer streaming applications. In: Proceedings of ACM Workshop on Advances in Peer-to-Peer Multimedia Streaming. 2005, 29–38
He Y, Liu Y. VOVO: VCR-oriented video-on-demand in large-scale peer-to-peer networks. IEEE Transactions on Parallel and Distributed Systems, 2009, 20(4): 528–539
Xu T, Ye B, Wang Q, Li W, Lu S, Fu X. APEX: a personalization framework to improve quality of experience for DVD-like functions in P2P VoD applications. In: Proceedings of IEEE International Workshop on Quality of Service. 2010, 1–9
Brampton A, MacQuire A, Fry M, Rai I A, Race N J P, Mathy L. Characterising and exploiting workloads of highly interactive videoon- demand. Multimedia Systems, 2009, 15(1): 3–17
Ekin A, Tekalp A M, Mehrotra R. Automatic soccer video analysis and summarization. IEEE Transactions on Image Process, 2003, 12(7): 796–807
Xu C, Zhang Y F, Zhu G, Rui Y, Lu H, Huang Q. Using webcast text for semantic event detection in broadcast sports video. IEEE Transactions on Multimedia, 2008, 10(7): 1342–1355
Tong X, Liu Q, Zhang Y, Lu H. Highlight ranking for sports video browsing. In: Proceedings of the 13th ACM International Conference on Multimedia. 2005, 519–522
Qian X, Wang H, Liu G, Hou X. HMM based soccer video event detection using enhanced mid-level semantic. Multimedia Tools and Application, 2012, 60(1): 233–255
Eldib MY, Zaid B S A, Zawbaa HM, El-Zahar M, El-Saban M. Soccer video summarization using enhanced logo detection. In: Proceedings of the 16th IEEE International Conference on Image Processing. 2009, 4345–4348
Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer, 2009
Page E S. Cumulative sum charts. Technometrics, 1961, 3(1): 1–9
Nikovski D, Jain A. Fast adaptive algorithms for abrupt change detection. Machine Learning, 2010, 79(3): 283–306
He Y, Shen G, Xiong Y, Guan L. Optimal prefetching scheme in P2P VoD applications with guided seeks. IEEE Transactions on Multimedia, 2009, 11(1): 138–151
Garcia R, G. Paneda X, Garcia V, Melendi D, Vilas M. Statistical characterization of a real video on demand service: user behaviour and streaming-media workload analysis. Simulation Modelling Practice and Theory, 2007, 15(6): 672–689
Claypool M, Le P, Waseda M, Brown D. Implicit interest indicators. In: Proceedings of International Conference on Intelligent User Interfaces. 2001, 33–40
Basseville M, Nikiforov I V. Detection of Abrupt Changes: Theory and Application. Prentice-Hall, 1993
Tjondronegoro D, Chen Y P. Knowledge-discounted event detection in sports video. IEEE Transactions on SystemsMan and Cybernetics, Part A: Systems and Humans, 2010, 40(5): 1009–1024
Chênes C, Chanel G, Soleymani M, Pun T. Highlight detection in movie scenes through inter-users, physiological linkage. Social Media Retrieval, 2010, 217–237
Zhu G, Huang Q, Xu C, Xing L, Gao W, Yao H. Human behavior analysis for highlight ranking in broadcast racket sports video. IEEE Transactions on Multimedia, 2007, 9(6): 1167–1182
Money A G, Agius H. ELVIS: entertainment-led video summaries. ACM Transactions on Multimedia Computing, Communications and Applications, 2010, 6(3): 1–17
Joho H, Staiano J, Sebe N, Jo J M. Looking at the viewer: analysing facial activity to detect personal highlights of multimedia contents. Multimedia Tools and Application, 2011, 51(2): 505–523
Chang B, Dai L, Cui Y, Xue Y. On feasibility of P2P on-demand streaming via empirical VoD user behavior analysis. In: Proceedings of the 28th IEEE International Conference on Distributed Computing Systems Workshops. 2008, 7–11
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Yang Zhao received the BS degree in computer science from University of Science and Technology of China (USTC) in 2009. He is currently a PhD candidate in the Department of Computer Science and Technology in USTC. His research interests include multimedia networks and vehicular ad hoc networks.
Ye Tian is an associate professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). He joined USTC in August 2008. He received his PhD degree from the Department of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK) in December 2007. His research interests include Internet and network measurement, information-centric networks, online social networks, and multimedia networks. He is a member of IEEE, and a senior member of China Computer Federation (CCF). He is currently serving as an associate editor for Frontiers of Computer Science.
Yong Liu is an associate professor at the Electrical and Computer Engineering Department of the Polytechnic Institute of New York University (NYU-Poly). He received his PhD degree from Electrical and Computer Engineering Department at the University of Massachusetts, Amherst, in May 2002. His general research interests lie in modeling, design and analysis of communication networks. His current research directions include Peer-to-Peer systems, overlay networks, network measurement, online social networks, and recommender systems. He is the winner of the IMC Best Paper Award in 2012, INFOCOM Best Paper Award in 2009, and the IEEE Communications Society Best Paper Award in Multimedia Communications in 2008. He is a senior member of IEEE and member of ACM. He is currently serving as an associate editor for IEEE/ACM Transactions on Networking, and Elsevier Computer Networks Journal.
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Zhao, Y., Tian, Y. & Liu, Y. Extracting viewer interests for automated bookmarking in video-on-demand services. Front. Comput. Sci. 9, 415–430 (2015). https://doi.org/10.1007/s11704-014-3490-2
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DOI: https://doi.org/10.1007/s11704-014-3490-2