Abstract
Along with the emerging focus of community-contributed videos on the web, there is a strong demand of a well-designed web video benchmark for the research of social network based video content analysis. The existing video datasets are challenged in two aspects: (1) as the data resource, most of them are narrowed for a specific task, either focusing on one content analysis task with limited scales, or focusing on the pure social network analysis without downloading video content. (2) as the evaluation platform, few of them pay attention to the potential bias introduced by the sampling criteria, therefore cannot fairly measure the task performance. In this paper, we release a large-scale web video benchmark named MCG-WEBV 2.0, which crawls 248,887 YouTube videos and their corresponding social network structure with 123,063 video contributors. MCG-WEBV 2.0 can be used to explore the fusion between content and network for several web video analysis tasks. Based on MCG-WEBV 2.0, we further explore the sampling bias lies in web video benchmark construction. While sampling a completely unbiased video benchmark from million-scale collection is unpractical, we propose a task-dependent measurement of such bias, which minimizes the correlation between the potential video sampling bias and the corresponding content analysis task, if such bias is unavoidable. Following this principle, we have shown several exemplar application scenarios in MCG-WEBV 2.0.
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References
Benevenuto F, Rodrigues T, Almeida V, Almeida J, Ross K (2009) Video interactions in online video social networks, ACM Transactions on Multimedia Computing. Commun Appl 5:1–25
Borghol Y, Mitra S, Ardon S, Carlsson N, Eager D, Mahanti A (2011) Characterizing and modeling popularity of user-generated videos. In: IFIP Performance
Cao J, Ngo CW, Zhang YD, Li JT Tracking web video topics:discovery, visualization and monitoring. IEEE Transaction on Circuits and Systems for Video Technology
Cao J, Zhang YD, Song YC, Chen ZN, Zhang X, Li JT (2009) Mcg-webv: A benchmark dataset for web video analysis. In: Technical Report, ICT-MCG-09-001
Cha M, Kwak H, Rodriguez P, Ahn YY, Moon S (2007) I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: ACM SIGCOMM Conference on Internet Measurement
Cheng X, Dale C, Liu J Q (2008) Statistics and social network of youtube videos. In: International Workshop on Quality of Service
Ding Y, Du Y, Hu Y, Liu Z, Wang L, Ross K, Ghose A (2011) Broadcast yourself: understanding youtube uploaders. In: ACM SIGCOMM conference on Internet measurement conference
Gao Y, Wang M, Zha Zh J, Shen Jl, Li XL, Wu XD (2013) Visual-textual joint relevance learning for tag-based social image search. In: IEEE Trans Image Process 22 (1)
Gill P, Arlitt M, Li Z, Mahanti A (2007) Youtube traffic characterization: a view from the edge. In: ACM SIGCOMM conference on Internet Measurement
Huang C, Li J, Ross KW (2007) Can internet video-on-demand be profitable? SIGCOMM Comput Commun Rev 37:133–144
Jiang YG, Ye GN, Chang SF, Ellis D, Loui AC (2011) Consumer video understanding: a benchmark database and an evaluation of human and machine performance, In: ACM International Conference on Multimedia Retrieval
Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: IEEE International Conference on Computer Vision and Pattern Recognition
Li H J, Liu B, Yi L, Guan Y, X Luo Zh (2014) On the Tag Localization of Web Video. In: Multimedia Systems
Liu L, Sun L, Rui Y, Shi Y, Yang S (2008) Web video topic discovery and tracking via bipartite graph reinforcement model. In: International Conference on World Wide Web
Pang L, Cao J, Zhang YD, Lin SX (2011) Leveraging collective wisdom for web video retrieval through heterogeneous community discovery. In: ACM International Conference on Multimedia
Song YC, Zhang YD, Cao J, Xia T, Liu W, Li JT Web video geolocation by geotagged social resources. IEEE Transaction on Multimedia
Sharma AS, Elidrisi M Classification of multimedia content using tags and focal points. In: Project Report of University Of Minnesota., http://www-users.cs.umn.edu/ankur/FinalReport_PR-1.pdf, 2009
Smeaton AF, Over P, Kraaij W (2006) Evaluation campaigns and trecvid. In: ACM International Workshop on Multimedia Information Retrieval
Song Y Ch, Zhang YD, Cao J, Tang JH, Gao XY, Li JT (2014) A unified geolocation framework for web videos. ACM Trans Intell Syst Technol (TIST) 5(3):49
Tang JH, Yan SCh, Hong RCh, Qi GJ, Chua TS (2009) Inferring Semantic Concepts from Community-contributed Images and Noisy Tags. In: ACM International Conference on Multimedia
Torralba A, Efros AA (2011) Unbiased look at dataset bias. In: IEEE International Conference on Computer Vision and Pattern Recognition
UCF 50 Human Action Dataset., http://server.cs.ucf.edu/vision/data/UCF50.rar/, 2010
Wu X, Hauptmann AG, Ngo CW (2007) Practical elimination of near-duplicates from web video search. In: ACM International Conference on Multimedia
Yu H, Zheng D, Zhao BY, Zheng W (2006) Understanding user behavior in large-scale video-on-demand systems. SIGOPS Oper Syst Rev 40:333–344
Zha ZhJ, Wang M, Zheng YT, Yang Y, Hong R Ch, Chua TS (2012) Interactive video indexing with statistical active learning. IEEE Trans Multimed 14(1):17–27
Zha ZhJ, Zhang HW et al (2013) Detecting group activities with multi-camera context. IEEE Trans Circ Syst Video Technol 23 (5):856–869
Zha ZhJ, Yang LJ, Mei T, Wang M, Wang ZF, Chua TS, Hua XSh (2010) Visual query suggestion: Towards Capturing User Intent in Internet Image Search. In: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMMCAP) 6(3)
Acknowledgments
This work is supported by the National High Technology Research and Development Program of China (2014AA015202), National Nature Science Foundation of China (61172153,61100087), National Key Technology Research and Development Program of China (2012BAH39B02).the Beijing New Star Project on Science & Technology (2007B071).
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Cao, J., Zhang, Y., Ji, R. et al. On application-unbiased benchmarking of web videos from a social network perspective. Multimed Tools Appl 75, 1543–1556 (2016). https://doi.org/10.1007/s11042-014-2245-2
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DOI: https://doi.org/10.1007/s11042-014-2245-2