A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attention-based video summarisation in rushes collection
Proceedings of the international workshop on TRECVID video summarization
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
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
The trecvid 2008 BBC rushes summarization evaluation
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
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The rushes is a collection of raw material videos. There are various redundancies, such as rainbow screen, clipboard shot, white/black view, and unnecessary re-take. This paper develops a set of solutions to remove these video redundancies as well as an effective system for video summarisation. We regard manual editing effects, e.g. clipboard shots, as differentiators in the visual language. A rushes video is therefore divided into a group of subsequences, each of which stands for a re-take instance. A graph matching algorithm is proposed to estimate the similarity between re-takes and suggests the best instance for content presentation. The experiments on the Rushes 2008 collection show that a video can be shortened to 4%-16% of the original size by redundancy detection. This significantly reduces the complexity in content selection and leads to an effective and efficient video summarisation system.