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

Bingqing Qu, Felicien Vallet, Jean Carrive, Guillaume Gravier
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pmefrmqsezb5zd3w7pf5k7fmxu" style="color: black;">2014 IEEE International Conference on Multimedia and Expo (ICME)</a> </i> &nbsp;
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 structure 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,
more &raquo; ... both the vocabulary of the grammar and the temporal organization of the words from the vocabulary. Using a set of basic event detectors and simple filtering techniques to detect repeating elements of interest, a symbolic representation of each episode is derived based on minimal domain knowledge. 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. Index Terms-TV program structuring, hierarchical structural grammar, symbolic representation, multiple sequence alignment, unsupervised and multimodal approach
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