Content description becomes important in the ubiquity of video content on the Web and consumer devices. Video classification is needed so that more appropriate description and search methods can be applied. This paper describes two methods for video content classification: a Nearest Neighbor (NN) method relying on domain knowledge and Hidden Markov Model (HMM) based method. Our approach stems from the observation that in different TV categories, there are different objects (e.g., face and text) trajectory patterns. Face and text tracking is applied to video segments to extract face and text trajectories. We used NN and HMM to classify a given video segment into predefined classes, e.g., commercial, news, situation comedy and soap. Our preliminary experimental results show classification accuracy of 75% for NN and over 80% for HMM based method on short video segments.
Twitter content classification
