A color-action perceptual approach to the classification of animated movies

Abstract

We address a particular case of video genre classification, namely the classification of animated movies. This task is achieved using two categories of content descriptors, temporal and color based, which are adapted to this particular content. Temporal descriptors, like rhythm or action, are quantifying the perception of the action content at different levels. Color descriptors are determined using color perception which is quantified in terms of statistics of color distribution, elementary hues, color properties (e.g. amount of light colors, cold colors, etc.) and color relationship. The potential of the proposed descriptors to the classification task has been proved through experimental tests conducted on more than 159 hours of video footage. Despite the high diversity of the video material, the proposed descriptors achieve an average precision and recall ratios up to 90% and 92%, respectively, and a global correct detection ratio up to 92%.