The segmentation of news video into story units

The segmentation 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 HMM (hidden Markov models) analysis to locate the 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, and over 89% in F₁ measure on scene/story boundary detection.