A Local Temporal Context-Based Approach for TV News Story Segmentation

Users are often interested in retrieving only a particular passage on a topic of interest to them. It is therefore necessary to split videos into shorter segments corresponding to appropriate retrieval units. We propose here a method based on a local temporal context for the segmentation of TV news videos into stories. First, we extract multiple descriptors which are complementary and give good insights about story boundaries. Once extracted, these descriptors are expanded with a local temporal context and combined by an early fusion process. The story boundaries are then predicted using machine learning techniques. We investigate the system by experiments conducted using TRECVID 2003 data and protocol of the story boundary detection task and we show that the extension of multimodal descriptors by a local temporal context approach improves results and our method outperforms the state of the art.