Audio/Visual Recurrences and Decision Trees for Unsupervised TV Program Structuring

Abstract

This paper addresses the problem of unsupervised TV program structuring. Program structuring allows direct and non linear access to the desired parts of a program. Our work addresses the structuring of programs like news, entertainment, shows, magazines... It is based on the detection of audio and visual recurrences. It proposes an effective classification and selection system, based on decision trees, that allows the detection of "separators" among these recurrences. Separators are short audio/visual sequences that delimit the different parts of a program. The decision trees are built based on attributes issued from techniques like applause detection, scenes segmentation, face/speaker detection and clustering. The approach has been evaluated on a 112 hours dataset corresponding to 169 episodes of TV programs.