TV News Story Segmentation Based on Semantic Coherence and Content Similarity

Misra, H., Hopfgartner, F. , Goyal, A., Punitha, P. and Jose, J. (2010) TV News Story Segmentation Based on Semantic Coherence and Content Similarity. In: 16th International Multimedia Modeling Conference, MMM 2010, Chongqing, China, 6-8 Jan 2010, pp. 347-357. ISBN 978642113000 (doi: 10.1007/978-3-642-11301-7_36)

Abstract

In this paper, we introduce and evaluate two novel approaches, one using video stream and the other using close-caption text stream, for segmenting TV news into stories. The segmentation of the video stream into stories is achieved by detecting anchor person shots and the text stream is segmented into stories using a Latent Dirichlet Allocation (LDA) based approach. The benefit of the proposed LDA based approach is that along with the story segmentation it also provides the topic distribution associated with each segment. We evaluated our techniques on the TRECVid 2003 benchmark database and found that though the individual systems give comparable results, a combination of the outputs of the two systems gives a significant improvement over the performance of the individual systems.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Hopfgartner, Dr Frank and Goyal, Mr Anuj and Misra, Dr Hemant
• Authors: Misra, H., Hopfgartner, F., Goyal, A., Punitha, P., and Jose, J.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Arts & Humanities > School of Humanities > Information Studies; College of Science and Engineering > School of Computing Science
• ISSN: 0302-9743
• ISBN: 978642113000
• Copyright Holders: Copyright © 2010 Springer-Verlag Berlin Heidelberg
• First Published: First published in Advances in Multimedia Modeling: 347-357
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher