Content-Based Video Description for Automatic Video Genre Categorization

In this paper, we propose an audio-visual approach to video genre categorization. It exploits audio, color, temporal and contour information, which are in general genre specific. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At temporal level, we asses action contents with respect to human perception. Further, color perception is quantified with statistics of color distribution, elementary hues, color properties and relationship of color. The final descriptor set determines statistics of contour geometry. Validation is performed on more than 91 hours of video footage and 7 common video genres. We obtain average precision and recall ratios within [87% − 100%] and [77% − 100%], respectively, while average correct classification is up to 97%. Additionally, we observe that movies displayed according to feature-based coordinates (we use a specially designed 3D browsing environment) tend to regroup with respect to genre, which has potential application with real content-based browsing systems (e.g. commercial video selling/rental platforms).