Publication Type
Conference Proceeding Article
Publication Date
7-2005
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 news stories with textual-visual concepts is significantly better than the co-clustering with either textual or visual concept alone.
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of 2005 IEEE International Conference on Multimedia and Expo, Amsterdam, July 6
Volume
2005
First Page
117
Last Page
120
ISBN
9780780393325
Identifier
10.1109/ICME.2005.1521374
Publisher
IEEE
City or Country
Amsterdam
Citation
WU, Xiao; NGO, Chong-wah; and LI, Qing.
Co-clustering of time-evolving news story with transcript and keyframe. (2005). Proceedings of 2005 IEEE International Conference on Multimedia and Expo, Amsterdam, July 6. 2005, 117-120.
Available at: https://ink.library.smu.edu.sg/sis_research/6552
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons