Definition
Video usage mining refers to analysis of user behaviors in large video databases.
Analysis of user behaviors in large video databases is an emergent problem. The growing importance of video in every day life (ex. Movie production) increases automatically the importance of video usage. To be able to cope with the abundance of available videos, users of these videos need intelligent software systems that fully utilize the rich source information hidden in user behaviors on large video data bases for retrieving and navigating through videos. In this paper, we present a framework for video usage mining to generate user profiles on a video search engine in the context of movie production. We propose a two level model based approach for modeling user behaviors on a video search engine. The first level aims at modeling and clustering user behavior on a single video sequence (intra video behavior), the second one aims at...
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References
S. Acharya, B. Smith, and P. Parnes, “Characterizing User Access to Videos on the World Wide Web,” Proceedings of Multimedia Computing and Networking, 2000, CA, USA.
I. Cadez, D. Heckerman, C. Meek, and P. Smyth, “Visualization of Navigation Patterns on a Web Site using Model Based Clustering,” Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, pp. 280–284.
A.I. Reuther and G. Meyer, “The Effect of Personality Type on the Usage of a Multimedia Engineering Education System,” Frontiers in Education, FIE'02, 32nd Annual, Vol. 1, November 2002, pp. T3A7–T3A12.
W. Wang and O.R. Zaiane, “Clustering Web Sessions by Sequence Alignment,” Proceedings of the 13th International Workshop on Database and Expert Systems Applications (DEXA'02), Aix-en-Provence, France.
B. Yu, W.-Y. Ma, K. Nahrstedt, and H-J. Zhang, “Video Summarization Based on User Log Enhanced Link Analysis,” Proceedings of Multimedia Modeling'03, Berkeley CA, USA, November 2003.
B. Mobasher, H. Dai, T. Luo, M. Nakagawa, Y. Sun, and J. Wiltshire, “Discovery of Aggregate Usage Profiles for Web Personalization,” Webkdd, Boston, MA, USA, 2000.
P. Branch, G. Egan, and B. Tonkin, “Modeling Interactive Behavior of a Video Based Multimedia System,” Proceedings of the IEEE International Conference on Communications, Vol. 2, June 1999, pp. 978–982.
S. Guha, R. Rastogi, and K. Shim, “CURE: An Efficient Clustering Algorithm for Large Databases,” SIGMOD'98, Seattle, WA, USA.
S. Guha, R. Rastogi, and K. Shim, “ROCK: A Robust Clustering Algorithm for Categorical Attributes,” Proceedings of the 15th International Conference on Data Engineering 1999, Syd006Eey, Australia.
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© 2008 Springer-Verlag
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Mongy, S., Bouali, F., Djeraba, C. (2008). Video Usage Mining. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_251
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DOI: https://doi.org/10.1007/978-0-387-78414-4_251
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74724-8
Online ISBN: 978-0-387-78414-4
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