A Contour-Color-Action Approach to Automatic Classication of Several Common Video Genres
Résumé
We address the issue of automatic video genre retrieval. We propose three categories of content descriptors, extracted at temporal, color and structural level. At temporal level, video content is described with visual rhythm, action content and amount of gradual transitions. Colors are globally described with statistics of color distribution, elementary hues, color properties and relationship. Finally, structural information is extracted at image level and histograms are built to describe contour segments and their relations. The proposed parameters are used to classify 7 common video genres, namely: animated movies/cartoons, commercials, documentaries, movies, music clips, news and sports. Experimental tests using several classification techniques and more than 91 hours of video footage prove the potential of these parameters to the indexing task: despite the similarity in semantic content of several genres, we achieve detection ratios ranging between 80-100%.