The Segmentation and Classification of Story Boundaries in News Video
Lekha Chaisorn, Tat‐Seng Chua · 2002
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 predefined 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.