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Scene Segmentation of Video Sequences

A scene is defined a group of video shots that are related to the same subject, e.g., chapters in movies, stories in news programs, etc. In this work, I have developed a general framework for temporal scene segmentation in various video domains. The developed framework is formulated in a statistical fashion and uses the Markov chain Monte Carlo (MCMC) technique to determine the boundaries between video scenes.  In this approach, a set of arbitrary scene boundaries are initialized at random locations and are automatically updated using two types of updates: diffusion and jumps. The major advantage of the proposed framework is two-fold: 1) it is able to find the weak boundaries as well as the strong boundaries, i.e., it does not rely on the fixed threshold; 2) it can be applied to different video domains.  The proposed scene segmentation framework has been applied on home videos and feature films, very promising results have been obtained for both domains.