Split and merge based story segmentation in news videos

Goyal, A., Punitha, P., Hopfgartner, F. and Jose, J.M. (2009) Split and merge based story segmentation in news videos. Lecture Notes in Computer Science, 5478, pp. 766-770. (doi: 10.1007/978-3-642-00958-7)

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Publisher's URL: http://dx.doi.org/10.1007/978-3-642-00958-7

Abstract

Segmenting videos into smaller, semantically related segments which ease the access of the video data is a challenging open research. In this paper, we present a scheme for semantic story segmentation based on anchor person detection. The proposed model makes use of a split and merge mechanism to find story boundaries. The approach is based on visual features and text transcripts. The performance of the system was evaluated using TRECVid 2003 CNN and ABC videos. The results show that the system is in par with state-of-the-art classifier based systems.

Item Type:Articles
Additional Information:The original publication is available at www.springerlink.com
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon
Authors: Goyal, A., Punitha, P., Hopfgartner, F., and Jose, J.M.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
ISSN:0302-9743

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