Improving retake detection by adding motion feature
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Automatic evaluation of video summaries
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Rushes video summarization based on spatio-temporal features
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Frontiers of Computer Science: Selected Publications from Chinese Universities
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During the post-production stage of film making, the film editor is faced with large amounts of unedited raw material, called rushes. Developing tools to view and organize this material is an important component of video processing. This paper describes an approach for summarizing rushes video based on the detection of repetitive sequences, using a variant of the Smith-Waterman algorithm to find matching subsequences. We rely on the evaluation methodology that has been introduced in the TRECVID BBC Rushes Summarization Task. We propose an automation of the manual TRECVID evaluation using machine learning techniques to train an automatic assessor. We compare the automatic assessor evaluation to the evaluations provided by the TRECVID manual assessors.