Abstract

This paper presents a novel technique for video shot detection, including hard cuts and gradual transitions. The method builds on various similarity criteria, namely histogram difference, motion estimation and distribution of intensity difference. While these metrics are well known, the novelty of the paper resides in the probabilistic framework to assess the similarity between two images. The a contrario framework, introduced in [1, 2] enables to control explicitly the number of false alarms given a background noise model. Experiments have been conducted on the Trecvid 2007 database: for all transitions (cuts and gradual), a recall of 0.95 and a precision of 0.96 was obtained.

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