Co-clustering of time-evolving news story with transcript and keyframe

Xiao Wu, Chong Wah Ngo, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

This paper presents techniques in clustering the sametopic 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.

Original languageEnglish
Title of host publicationIEEE International Conference on Multimedia and Expo, ICME 2005
Pages117-120
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes
EventIEEE International Conference on Multimedia and Expo, ICME 2005 - Amsterdam, Netherlands
Duration: 6 Jul 20058 Jul 2005

Publication series

NameIEEE International Conference on Multimedia and Expo, ICME 2005
Volume2005

Conference

ConferenceIEEE International Conference on Multimedia and Expo, ICME 2005
Country/TerritoryNetherlands
CityAmsterdam
Period6/07/058/07/05

ASJC Scopus subject areas

  • Engineering(all)

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