Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The trecvid 2007 BBC rushes summarization evaluation pilot
Proceedings of the international workshop on TRECVID video summarization
Dimensionality reduction for heterogeneous dataset in rushes editing
Pattern Recognition
Brief and high-interest video summary generation: evaluating the AT&T labs rushes summarizations
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Exploring the utility of fast-forward surrogates for bbc rushes
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
A simplified approach to rushes summarization
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
ELVIS: Entertainment-led video summaries
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A framework for video abstraction systems analysis and modelling from an operational point of view
Multimedia Tools and Applications
Sequence-kernel based sparse representation for amateur video summarization
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
Video summarization: techniques and classification
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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This paper describes a system for selecting excerpts from unedited video and presenting the excerpts in a short summary video for efficiently understanding the video contents. Color and motion features are used to divide the video into segments where the color distribution and camera motion are similar. Segments with and without camera motion are clustered separately to identify redundant video. Audio features are used to identify clapboard appearances for exclusion. Representative segments from each cluster are selected for presentation. To increase the original material contained within the summary and reduce the time required to view the summary, selected segments are played back at a higher rate based on the amount of detected camera motion in the segment. Pitch-preserving audio processing is used to better capture the sense of the original audio. Metadata about each segment is overlayed on the summary to help the viewer understand the context of the summary segments in the original video.