CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Clustering Web Sessions by Sequence Alignment
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Video summarization based on user log enhanced link analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Why and how CARPE should be personal?
CARPE '05 Proceedings of the 2nd ACM workshop on Continuous archival and retrieval of personal experiences
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Community annotation and remix: a research platform and pilot deployment
Proceedings of the 1st ACM international workshop on Human-centered multimedia
Mixture of KL subspaces for relevance feedback
Multimedia Tools and Applications
A study on video viewing behavior: application to movie trailer miner
International Journal of Parallel, Emergent and Distributed Systems
EUC'06 Proceedings of the 2006 international conference on Embedded and Ubiquitous Computing
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The analysis of user behaviors in large video databases is an emergent problem. The growing importance of video in every day life (ex. Movie production) is bound to the importance of video usage. In order 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 to retrieve and navigate 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 suggest a two levels 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 modeling and clustering user behavior on a set of video sequences (inter video behavior). Based on this representation we have developed a two phase clustering algorithm that fits these data.