|
ABSTRACT
Popular content in video sharing web sites (e.g., YouTube) is usually duplicated. Most scholars define near-duplicate video clips (NDVC) based on non-semantic features (e.g., different image/audio quality), while a few also include semantic features (different videos of similar content). However, it is unclear what features contribute to the human perception of similar videos. Findings of two large scale online surveys (N = 1003) confirm the relevance of both types of features. While some of our findings confirm the adopted definitions of NDVC, other findings are surprising. For example, videos that vary in visual content - by overlaying or inserting additional information - may not be perceived as near-duplicate versions of the original videos. Conversely, two different videos with distinct sounds, people, and scenarios were considered to be NDVC because they shared the same semantics (none of the pairs had additional information). Furthermore, the exact role played by semantics in relation to the features that make videos alike is still an open question. In most cases, participants preferred to see only one of the NDVC in the search results of a video search query and they were more tolerant to changes in the audio than in the video tracks. Finally, we propose a user-centric NDVC definition and present implications for how duplicate content should be dealt with by video sharing websites.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
Basharat, A., Zhai, Y., and Shan, M. Content based video matching using spatiotemporal volumes,. Journal of Computer Vision and Image Understanding 110, 3 (June 2008), 360--377.
|
| |
2
|
Benevenuto, F., Duarte, F., Rodrigues, T., Almeida, V. A., Almeida, J. M., and Ross, K. W. Understanding video interactions in youtube. In MM '08: Proceeding of the 16th ACM international conference on Multimedia (New York, NY, USA, 2008), ACM, pp. 761---764.
|
| |
3
|
Celebi, M. E., and Aslandogan, Y. A. Human perception-driven, similarity-based access to image databases. In Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference (Clearwater Beach, Florida, May 15-17 2005), I. Russell and Z. Markov, Eds., pp. 245--251.
|
| |
4
|
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., and Moon, S. I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system. In IMC '07: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement (New York, NY, USA, 2007), ACM, pp. 1--14.
|
| |
5
|
Gill, P., Li, Z., Arlitt, M., and Mahanti, A. Characterizing users sessions on youtube. In Proceedings of SPIE/ACM Conference on Multimedia Computing and Networking (MMCN) (San Jose, CA, USA, January 30-31 2008).
|
| |
6
|
Guyader, N., Borgne, H. L., Hérault, J., and Guérin-Dugué, A. Towards the introduction of human perception in a natural scene classification system. In Proceedings of Neural Networks for Signal Processing (Martigny, Switzerland, September 4-6 2002), pp. 385--394.
|
| |
7
|
Halvey, M. J., and Keane, M. T. Exploring social dynamics in online media sharing. In WWW '07: Proceedings of the 16th international conference on World Wide Web (New York, NY, USA, 2007), ACM, pp. 1273--1274.
|
| |
8
|
Hsu, W. H., Kennedy, L. S., and Chang, S.-F. Video search reranking via information bottleneck principle. In MULTIMEDIA '06: Proceedings of the 14th annual ACM international conference on Multimedia (New York, NY, USA, 2006), ACM, pp. 35--44.
|
| |
9
|
Kruitbosch, G., and Nack, F. Broadcast yourself on youtube: really? In HCC '08: Proceeding of the 3rd ACM international workshop on Human-centered computing (New York, NY, USA, 2008), ACM, pp. 7--10.
|
| |
10
|
Maia, M., Almeida, J., and Almeida, V. Identifying user behavior in online social networks. In SocialNets '08: Proceedings of the 1st workshop on Social network systems (New York, NY, USA, 2008), ACM, pp. 1--6.
|
| |
11
|
Payne, J. S., and Stonham, T. J. Can texture and image content retrieval methods match humanperception? In Proceedings of Intelligent Multimedia, Video and Speech Processing (Hong Kong, China, 2001), pp. 154--157.
|
| |
12
|
Rui, Y., Huang, T., and Chang, S. Image retrieval: current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation 10, 4 (April 1999), 39--62.
|
| |
13
|
Shao, J., Shen, H. T., and Zhou, X. Challenges and techniques for effective and efficient similarity search in large video databases. In Proceedings of the VLDB Endowment (2008), vol. 1, pp. 1598--1603.
|
| |
14
|
Shen, H. T., Zhou, X., Huang, Z., Shao, J., and Zhou, X. Uqlips: a real-time near-duplicate video clip detection system. In VLDB '07: Proceedings of the 33rd international conference on Very large data bases (2007), VLDB Endowment, pp. 1374--1377.
|
| |
15
|
Sheskin, D. J. Handbook of Parametric and Nonparametric Statistical Procedures, 1193 pp. ed. Chapman & Hall/CRC, Boca Raton, Florida, USA, 2004.
|
| |
16
|
Wu, X., Hauptmann, A. G., and Ngo, C.-W. Practical elimination of near-duplicates from web video search. In MULTIMEDIA '07: Proceedings of the 15th international conference on Multimedia (New York, NY, USA, 2007), ACM, pp. 218--227.
|
|