A Two-Level Multi-Modal Approach for Story Segmentation of Large News Video Corpus

This paper presents an enhanced work from our previous paper [Chaisorn et al. 2002]. The system is enhanced to perform news story segmentation on a large video corpus used in TRECVID 2003 evaluation. We use a combination of features include visual-based features such as color, object-based features such as face, video-text, temporal features such as audio and motion, and semantic feature such as cue-phrases. We employ Decision Tree and specific detectors to perform shot classification/tagging. We use the shot category information along with two temporal features to identify story boundaries using HMM (Hidden Markov Models). A heuristic rules-based technique is applied to classify each detected story into “news” or “misc”.