While video content is often stored in rather large files or broadcasted in continuous streams, users are often interested in retrieving\r\nonly a particular passage on a topic of interest to them. It is, therefore, necessary to split video documents or streams into\r\nshorter segments corresponding to appropriate retrieval units. We propose here a method for the automatic segmentation of TV\r\nnews videos into stories. A-multiple-descriptor based segmentation approach is proposed. The selected multimodal features are\r\ncomplementary and give good insights about story boundaries. Once extracted, these features are expanded with a local temporal\r\ncontext and combined by an early fusion process. The story boundaries are then predicted using machine learning techniques.We\r\ninvestigate the system by experiments conducted using TRECVID 2003 data and protocol of the story boundary detection task,\r\nand we show that the proposed approach outperforms the state-of-the-art methods while requiring a very small amount of manual\r\nannotation.
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