Content-based inference of hierarchical structural grammar for recurrent TV programs using multiple sequence alignment

Bingqing Qu 1, 2 Félicien Vallet 2 Jean Carrive 2 Guillaume Gravier 1
1 TEXMEX - Multimedia content-based indexing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : 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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Bingqing Qu, Félicien Vallet, Jean Carrive, Guillaume Gravier. Content-based inference of hierarchical structural grammar for recurrent TV programs using multiple sequence alignment. IEEE International Conference on Multimedia and Expo, Jul 2014, Chengdu, China. ⟨hal-01026335⟩

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