Meta-data framework for constructing individualized video digest

This paper presents a framework for providing video digests that are personalized by profiles of individual users. Video contents have meta-data described manually from a set of predefined keywords that have temporal duration. Content profiles are prepared by a provider, which are vectors of the importance value of keywords, and only one should be selected by a user. In addition, a user profile is collected by the user, which has the same components. The importance scores of an image sequence along the time axis can be calculated from a combination of these profiles. Finally, the video clips can be collected as the video digest from the whole contents, which have higher importance scores than a threshold transformed from the length of the user requirement.

[1]  Bernard Mérialdo,et al.  Automatic construction of personalized TV news programs , 1999, MULTIMEDIA '99.

[2]  Tomonari Kamba,et al.  Learning Personal Preferences on Online Newspaper Articles from User Behaviors , 1997, Comput. Networks.

[3]  Boon-Lock Yeo,et al.  Extracting story units from long programs for video browsing and navigation , 1996, Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems.

[4]  Michael A. Smith,et al.  Video skimming and characterization through the combination of image and language understanding techniques , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Tomio Echigo,et al.  Video summarization using reinforcement learning in eigenspace , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).