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. © Springer-Verlag Berlin Heidelberg 2009.
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CITATION STYLE
Goyal, A., Punitha, P., Hopfgartner, F., & Jose, J. M. (2009). Split and merge based story segmentation in news videos. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5478 LNCS, pp. 766–770). https://doi.org/10.1007/978-3-642-00958-7_82
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