C4.5: programs for machine learning
C4.5: programs for machine learning
Semantic analysis for video contents extraction—spotting by association in news video
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning user's preferences by analyzing Web-browsing behaviors
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Learning video browsing behavior and its application in the generation of video previews
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Sharing video browsing style by associating browsing behavior with low-level features of videos
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
The landscape of time-based visual presentation primitives for richer video experience
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
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We have increasingly more opportunities to use video for our knowledge work, such as monitoring events, reflecting on physical performances, learning subject matter, or analyzing scientific experimental phenomena. In such ill-defined situation, users often create their own browsing styles to explore the videos because the domain knowledge of contents is not useful, and then the users interact with videos according to their browsing style. However, such kind of tacit knowledge, which is acquired through user's experiences, has not been well managed. The goal of our research is to share and reuse tacit knowledge, and then create new knowledge by composing them in video browsing. This paper describes the notion of reusing habitual behavior of video browsing, and presents examples of composing these behaviors to create new video browsing styles.