Co-clustering of time-evolving news story with transcript and keyframe
This paper presents techniques in clustering the same-topic news stories according to event themes. We model the relationship of stories with textual and visual concepts under the representation of bipartite graph. The textual and visual concepts are extracted respectively from speech transcripts an...
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sg-smu-ink.sis_research-75552022-01-10T03:38:27Z Co-clustering of time-evolving news story with transcript and keyframe WU, Xiao NGO, Chong-wah LI, Qing This paper presents techniques in clustering the same-topic news stories according to event themes. We model the relationship of stories with textual and visual concepts under the representation of bipartite graph. The textual and visual concepts are extracted respectively from speech transcripts and keyframes. Co-clustering algorithm is employed to exploit the duality of stories and textual-visual concepts based on spectral graph partitioning. Experimental results on TRECVID-2004 corpus show that the co-clustering of news stories with textual-visual concepts is significantly better than the co-clustering with either textual or visual concept alone. 2005-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6552 info:doi/10.1109/ICME.2005.1521374 https://ink.library.smu.edu.sg/context/sis_research/article/7555/viewcontent/10.1.1.570.2727__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Graphics and Human Computer Interfaces |
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Databases and Information Systems Graphics and Human Computer Interfaces WU, Xiao NGO, Chong-wah LI, Qing Co-clustering of time-evolving news story with transcript and keyframe |
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This paper presents techniques in clustering the same-topic news stories according to event themes. We model the relationship of stories with textual and visual concepts under the representation of bipartite graph. The textual and visual concepts are extracted respectively from speech transcripts and keyframes. Co-clustering algorithm is employed to exploit the duality of stories and textual-visual concepts based on spectral graph partitioning. Experimental results on TRECVID-2004 corpus show that the co-clustering of news stories with textual-visual concepts is significantly better than the co-clustering with either textual or visual concept alone. |
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text |
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WU, Xiao NGO, Chong-wah LI, Qing |
author_facet |
WU, Xiao NGO, Chong-wah LI, Qing |
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WU, Xiao |
title |
Co-clustering of time-evolving news story with transcript and keyframe |
title_short |
Co-clustering of time-evolving news story with transcript and keyframe |
title_full |
Co-clustering of time-evolving news story with transcript and keyframe |
title_fullStr |
Co-clustering of time-evolving news story with transcript and keyframe |
title_full_unstemmed |
Co-clustering of time-evolving news story with transcript and keyframe |
title_sort |
co-clustering of time-evolving news story with transcript and keyframe |
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Institutional Knowledge at Singapore Management University |
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2005 |
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https://ink.library.smu.edu.sg/sis_research/6552 https://ink.library.smu.edu.sg/context/sis_research/article/7555/viewcontent/10.1.1.570.2727__1_.pdf |
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