Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Skimming rushes video using retake detection
Proceedings of the international workshop on TRECVID video summarization
A Comparison of Distance Measures for Clustering Video Sequences
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Detecting and clustering multiple takes of one scene
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Automatic evaluation of video summaries
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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We compare two methods for selecting segments to be included in a video skim, using lists of relevant as well as redundant segments created from different visual features as input. One approach is rule-based, and creates a weighted sum of the input relevances. The other is HMM based, using a model trained on the TRECVID 2007 rushes data. The redundant segments are created from detection of repeated takes and junk content, the selected segments from visual activity and face detection. The results show that the approaches create very short summaries which only contain a part of the relevant information in the video, but reach very high scores in terms of the usability measures non-duplicates, non-junk and pleasant tempo. The HMM based approach contains more information despite shorter duration of the summaries.