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International Journal of Digital Multimedia Broadcasting
Volume 2012 (2012), Article ID 732514, 11 pages
Research Article

Automatic Story Segmentation for TV News Video Using Multiple Modalities

UJF-Grenoble 1/UPMF-Grenoble 2/Grenoble INP, CNRS, LIG UMR 5217, 38041 Grenoble, France

Received 18 November 2011; Revised 13 March 2012; Accepted 12 April 2012

Academic Editor: Werner Bailer

Copyright © 2012 Émilie Dumont and Georges Quénot. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


While video content is often stored in rather large files or broadcasted in continuous streams, users are often interested in retrieving only a particular passage on a topic of interest to them. It is, therefore, necessary to split video documents or streams into shorter segments corresponding to appropriate retrieval units. We propose here a method for the automatic segmentation of TV news videos into stories. A-multiple-descriptor based segmentation approach is proposed. The selected multimodal features are complementary and give good insights about story boundaries. Once extracted, these features 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 proposed approach outperforms the state-of-the-art methods while requiring a very small amount of manual annotation.