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

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%.