Story segmentation in TV news broadcast

Segmentation of TV news broadcast into semantically meaningful stories is an essential pre-requisite for a wide range of video analytics applications. In this work we have introduced a hybrid approach for news story segmentation based on conditional random fields (CRFs). The story boundary detection problem is converted into a shot classification problem by classifying video shots into either of the four categories. These are start shot, end shot and middle shots of a story or single shot story. To achieve this classification, we have introduced two new features. These are overlay text based semantic similarity and grid-wise edge orientation histogram. The first feature measures the semantic similarity between video shots by linking them through a set of web news articles. We use overlay text with their relevance as weight to link a set of articles with the video shots. The second feature captures the variations in presentation formats. The CRF model effectively combines these two features to model the news stories. Experimental results on approximately 50 hours of news videos demonstrates the efficiency of the proposed features. We were able to achieve an F1 score of 81% with our proposed features.