A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Automatically estimating number of scenes for rushes summarization
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Automated video program summarization using speech transcripts
IEEE Transactions on Multimedia
Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning
IEEE Transactions on Multimedia
A novel video key-frame-extraction algorithm based on perceived motion energy model
IEEE Transactions on Circuits and Systems for Video Technology
VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method
Pattern Recognition Letters
Cross media hyperlinking for search topic browsing
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multi-video summary and skim generation of sensor-rich videos in geo-space
Proceedings of the 3rd Multimedia Systems Conference
Video summarization based on balanced AV-MMR
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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Most previous works on video summarization target on a single video document. With the popularity of video corpus (e.g. news video archives) and web videos, video article that consists of a set of relevant videos are frequently confronted by users. By the traditional single-document summarization, these videos are treated independently and the results are usually redundant due to the lack of inter-video analysis. To efficiently manage video articles, in this paper, we propose an approach for multi-document video summarization by exploring the redundancy between different videos. The importance of keyframes is first measured by the content inclusion based on intra- and inter-video similarities. We then propose a Minimum Description Length (MDL) for automatically determining the appropriate length of the summary. Finally a video summary is generated for users to browse the content of the whole video article. We show that multidocument video summarization presents more elegant and informative summaries compared with single-document approach.