CO-CLUSTERING OF TIME-EVOLVING NEWS STORY WITH TRANSCRIPT AND KEYFRAME (WedPmPO1)

Author(s) : Xiao Wu (City University of Hong Kong, Hong Kong) Chong Wah Ngo (City University of Hong Kong, Hong Kong) Qing Li (City University of Hong Kong, Hong Kong)

Abstract : 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 stories with textual-visual concepts is significantly better than the co-clustering with either textual or visual concept alone.

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