Rushes video summarization and evaluation

 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.