Modeling timing features in broadcast news video classification

Broadcast news programs are well-structured video, and timing can be a strong predictor for specific types of news reports. However, learning a classifier using timing features may not be an easy task when training data are noisy. We approach the problem from the generative model perspective, and approximate the class density in a non-parametric fashion. The results show that timing is a simple but extremely effective feature, and our method can achieve significantly better performance than a discriminative classifier.