Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Topic detection in cross-media: a semi-supervised co-clustering approach

Topic detection in cross-media: a semi-supervised co-clustering approach With the rapid development of social media, the topics emerge and propagate in a variety of media websites. Although much work has been done since NIST proposed the problem of topic detection and tracking (TDT), most of them focus on single media data and are mainly based on unsupervised clustering method, which does not use some side information to help detecting topics. Therefore, traditional TDT approaches are not competent for cross-media topic detection. To efficiently use the information contained in multi-modal data from different sources and the prior knowledge, we propose a semi-supervised co-clustering approach for cross-media topic detection by a constrained non-negative matrix factorization. The correctness and convergence of our approach are proved to demonstrate its mathematical rigorousness. Experiments on the cross-media dataset verify the effectiveness of our proposed approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Topic detection in cross-media: a semi-supervised co-clustering approach

Download PDF
13 pages

Loading next page...
 
/lp/springer-journals/topic-detection-in-cross-media-a-semi-supervised-co-clustering-3BMYYBzBh4

References (7)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer-Verlag London
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl. Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-014-0056-x
Publisher site
See Article on Publisher Site

Abstract

With the rapid development of social media, the topics emerge and propagate in a variety of media websites. Although much work has been done since NIST proposed the problem of topic detection and tracking (TDT), most of them focus on single media data and are mainly based on unsupervised clustering method, which does not use some side information to help detecting topics. Therefore, traditional TDT approaches are not competent for cross-media topic detection. To efficiently use the information contained in multi-modal data from different sources and the prior knowledge, we propose a semi-supervised co-clustering approach for cross-media topic detection by a constrained non-negative matrix factorization. The correctness and convergence of our approach are proved to demonstrate its mathematical rigorousness. Experiments on the cross-media dataset verify the effectiveness of our proposed approach.

Journal

International Journal of Multimedia Information Retrieval – Springer Journals

Published: May 11, 2014

There are no references for this article.