Content-based inference of hierarchical structural grammar for recurrent TV programs using multiple sequence alignment
Résumé
Recently, unsupervised approaches were introduced to analyze the structure of TV programs, relying on the discovery of repeated elements within a program or across multiple episodes of the same program. These methods can discover key repeating elements, such as jingles and separators, however they cannot infer the entire struc- ture of a program. In this paper, we propose a hierarchical use of grammatical inference to yield a temporal grammar of a program from a collection of episodes, discovering both the vocabulary of the grammar and the temporal organization of the words from the vocab- ulary. Using a set of basic event detectors and simple filtering tech- niques to detect repeating elements of interest, a symbolic represen- tation of each episode is derived based on minimal domain knowl- edge. Grammatical inference based on multiple sequence alignment is then used in a hierarchical manner to provide a temporal grammar of the program at various levels of details. Experimental validation is performed on 3 distinct types of programs on 4 datasets. Qualitative analyses show that the grammars inferred at the different levels of the hierarchy are relevant and can be obtained from a fairly limited number of episodes.
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