The segmentation and classification of news video into single-story semantic units is a challenging problem. This research proposes a two-level, multi-modal framework to tackle this problem. The video is analyzed at the shot and story unit (or scene) levels using a variety of features and techniques. At the shot level, we employ a Decision Tree to classify the shot into one of 13 pre-defined categories. At the scene level, we perform the HMM (Hidden Markov Models) analysis to eliminate shot classification errors and to locate story boundaries. We test the performance of our system using two days of news video obtained from the MediaCorp of Singapore. Our initial results indicate that we could achieve a high accuracy of over 95 % for shot classification. The use of HMM analysis helps to improve the accuracy of the shot classification and achieve over 89% accuracy on story segmentation.
In REF , authors noticed 13 categories of shots in TV-news (interview, anchor, commercial, etc) and trained a decision tree to classify shots, then a Hidden Markov Model (HMM) to correct classification errors.
The segmentation and classification of story boundaries in news video
in IEEE International Conference on Multimedia and Expo
Computer Science