CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic replay generation for soccer video broadcasting
Proceedings of the 12th annual ACM international conference on Multimedia
Dogear: Social bookmarking in the enterprise
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
From social bookmarking to social summarization: an experiment in community-based summary generation
Proceedings of the 12th international conference on Intelligent user interfaces
Network-aware identification of video clip fragments
Proceedings of the 6th ACM international conference on Image and video retrieval
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Social summarization: does social feedback improve access to speech data?
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Video retrieval using an EDL-Based timeline
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Easy on that trigger dad: a study of long term family photo retrieval
Personal and Ubiquitous Computing
Content redundancy in YouTube and its application to video tagging
ACM Transactions on Information Systems (TOIS)
'Mind the gap': evaluating user physiological response for multi-genre video summarisation
BCS-HCI '13 Proceedings of the 27th International BCS Human Computer Interaction Conference
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Identifying highlights in multimedia content such as video and audio is currently a very difficult technical problem. We present and evaluate a novel algorithm that identifies highlights by combining content analysis with Web 2.0 data mining techniques. We exploit the fact that popular content tends to be redundantly uploaded onto community sharing sites. Our "social summarization" technique first identifies overlaps in uploaded scenes and then uses the upload frequency of each video scene to compute that scene's importance in the complete video. Our user evaluation shows the reliability of the technique: scenes automatically selected by our method are agreed by experts to be the most relevant.