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Animated movie genre detection using symbolic fusion of text and image descriptors

Abstract : This paper addresses the automatic movie genre classification in the specific case of animated movies. Two types of information are used. The first one are movie synopsis. For each genre, a symbolic representation of a thematic intensity is extracted from synopsis. Addressed visually, movie content is described with symbolic representations of different mid-level color and activity features. A fusion between the text and image descriptions is performed using a set of symbolic rules conveying human expertise. The approach is tested on a set of 107 animated movies in order to estimate their "drama" character. It is observed that the text-image fusion achieves a precision up to 78% and a recall of 44%.
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Contributor : Patrick Lambert <>
Submitted on : Monday, September 17, 2012 - 2:07:24 PM
Last modification on : Friday, November 6, 2020 - 3:34:51 AM
Long-term archiving on: : Friday, December 16, 2016 - 2:03:28 PM


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  • HAL Id : hal-00732733, version 1



Gregory Païs, Patrick Lambert, Françoise Deloule, Daniel Beauchêne, Bogdan Ionescu. Animated movie genre detection using symbolic fusion of text and image descriptors. 10th International Workshop on Content-Based Multimedia Indexing (CBMI 2012), Jun 2012, France. pp.1-5. ⟨hal-00732733⟩



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