<XML><RECORDS><RECORD><REFERENCE_TYPE>3</REFERENCE_TYPE><REFNUM>9279</REFNUM><AUTHORS><AUTHOR>Misra,H.</AUTHOR><AUTHOR>Hopfgartner,F.</AUTHOR><AUTHOR>Goyal,A.</AUTHOR><AUTHOR>Punitha,P.</AUTHOR><AUTHOR>Jose,J.M.</AUTHOR></AUTHORS><YEAR>2010</YEAR><TITLE>TV news story segmentation based on semantic coherence and content similarity</TITLE><PLACE_PUBLISHED>The 16th International Conference on Multimedia Modeling</PLACE_PUBLISHED><PUBLISHER>N/A</PUBLISHER><LABEL>Misra:2010:9279</LABEL><KEYWORDS><KEYWORD>TV news segmentation</KEYWORD></KEYWORDS<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.</ABSTRACT></RECORD></RECORDS></XML>