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

Gregory Païs (1) , Patrick Lambert (1) , Françoise Deloule (1) , Daniel Beauchêne (1) , Bogdan Ionescu (2)

1 LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance
2 LAPI - Laboratorul de Analiza si Prelucrarea Imaginilor [Bucarest]

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%.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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⟩