Thesis
TV programs have an underlying structure that is lost when these are broadcasted. The linear mode is the only available reading mode when viewing programs recorded using a Personal Video Recorder or through a TV-on-Demand service. The fast-forward/backward functions are the only available tools for browsing. In this context, program structuring becomes important in order to provide users with novel and useful browsing features. In addition to advanced browsing features, TV program structuring can also be used for summarization, indexing and querying, archiving, etc.
This thesis addresses the problem of unsupervised TV program structuring. The idea is to automatically recover the original structure of the program by finding the start time of each part composing it. The proposed approach is completely unsupervised and addresses a large category of programs like TV games, magazines, news.... It is based on the detection of "separators" which are short audio/visual sequences that delimit the different parts of a program. To do so, audio and visual recurrences are first detected from a set of episodes of a same program. In order to extract the separators, the recurrences are then classified using decision trees. These are built based on attributes issued from techniques like applause detection, scenes segmentation, face and speaker detection and clustering.
The proposed approach has been evaluated on 112 hours of real data corresponding to 169 episodes of 11 French TV programs.
Type:
Thesis
Date:
2013-05-21
Department:
Data Science
Eurecom Ref:
4010
Copyright:
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