SVM-based video segmentation and annotation of lectures and conferences

This paper presents a classification system for video lectures and conferences based on Support Vector Machines (SVM). The aim is to classify videos into four different classes (talk, presentation, blackboard, mix). On top of this, the system further analyses presentation segments to detect slide transitions, animations and dynamic content such as video inside the presentation. The developed approach uses various colour and facial features from two different datasets of several hundred hours of video to train an SVM classifier. The system performs the classification on frame-by-frame basis and does not require precomputed shotcut information. To avoid over-segmentation and to take advantage of the temporal correlation of succeeding frames, the results are merged every 50 frames into a single class. The presented results prove the robustness and accuracy of the algorithm. Given the generality of the approach, the system can be easily adapted to other lecture datasets.