Features with Feelings - Incorporating User Preferences in Video Categorization

Rapid growth of video content over internet has necessitated an immediate need to organize these large databases into meaningful categories. In this paper, we explore the benefits of leveraging social attitudes (beliefs, opinions, interests and evaluations of people) with machine learning concepts (audio/video features) in the challenging and pressing task of organization of online video databases. Through the analysis of view counts, we model social participation (people's choices) towards a video's contents. Observations reveal that viewership patterns are correlated with video genres. We propose logistic growth models to characterize videos based on usage and obtain a probability of video category. We then combine these subjectively assessed priors with likelihood of video class (as estimated from objective audio/video features) to establish the final category in a Bayesian framework. We provide a comparitive analysis of classification accuracies when a) categories are known a priori b) when they are not known a priori. Experimentally, we establish improvement in classification accuracy upon incorporating social attitudes with state-of-the-art audio/video features.