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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Main Authors: WU, Xiao, NGO, Chong-wah, LI, Qing
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2005
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Online Access: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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spelling 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Graphics and Human Computer Interfaces
spellingShingle 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
description 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.
format text
author WU, Xiao
NGO, Chong-wah
LI, Qing
author_facet WU, Xiao
NGO, Chong-wah
LI, Qing
author_sort 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
publisher Institutional Knowledge at Singapore Management University
publishDate 2005
url 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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