Automatically estimating number of scenes for rushes summarization | Zendy

Koji Yamasaki | Zendy; Koichi Shinoda | Zendy; Sadaoki Furui | Zendy

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Automatically estimating number of scenes for rushes summarization

Author(s) -

Koji Yamasaki,

Koichi Shinoda,

Sadaoki Furui

Publication year - 2008

Publication title -

tokyo tech research repository (tokyo institute of technology)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/1463563.1463587

Subject(s) - automatic summarization , computer science , selection (genetic algorithm) , set (abstract data type) , shot (pellet) , artificial intelligence , mixture model , task (project management) , component (thermodynamics) , computer vision , gaussian , pattern recognition (psychology) , chemistry , physics , organic chemistry , quantum mechanics , thermodynamics , programming language , management , economics

This paper describes our video summarization system using a model selection technique to estimate the optimal number of scenes for a summary. It uses a minimum description length as a model selection criterion and carries out two-stage estimation. First, we estimate the number of scenes in each shot, and then we estimate the number of scenes in a whole video clip. We model a set of scenes with a Gaussian mixture model, where the mixture component is assumed to represent one scene. Our system was evaluated in the TRECVID 2008 rushes summarization task, where the test video set was unedited materials provided by the BBC. Our scores were about the same as the average of all the participants for the eight evaluation measures.

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