Effective Multimodality Fusion Framework for Cross-Media Topic Detection

doi:10.1109/TCSVT.2014.2347551

	Effective Multimodality Fusion Framework for Cross-Media Topic Detection
	Chu, Lingyang 1; Zhang, Yanyan 2; Li, Guorong 2; Wang, Shuhui 1; Zhang, Weigang 3; Huang, Qingming 1
	2016-03-01
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
卷号	26 期号:3 页码:556-569
摘要	Due to the prevalence of We-Media, information is quickly published and received in various forms anywhere and anytime through the Internet. The rich cross-media information carried by the multimodal data in multiple media has a wide audience, deeply reflects the social realities, and brings about much greater social impact than any single media information. Therefore, automatically detecting topics from cross media is of great benefit for the organizations (i.e., advertising agencies and governments) that care about the social opinions. However, cross-media topic detection is challenging from the following aspects: 1) the multimodal data from different media often involve distinct characteristics and 2) topics are presented in an arbitrary manner among the noisy web data. In this paper, we propose a multimodality fusion framework and a topic recovery (TR) approach to effectively detect topics from cross-media data. The multimodality fusion framework flexibly incorporates the heterogeneous multimodal data into a multimodality graph, which takes full advantage from the rich cross-media information to effectively detect topic candidates (T.C.). The TR approach solidly improves the entirety and purity of detected topics by: 1) merging the T.C. that are highly relevant themes of the same real topic and 2) filtering out the less-relevant noise data in the merged T.C. Extensive experiments on both single-media and cross-media data sets demonstrate the promising flexibility and effectiveness of our method in detecting topics from cross media.
关键词	Cross-media fusion multimodality topic detection topic recovery (TR) We-Media
DOI	10.1109/TCSVT.2014.2347551
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China (973 Program)[2012CB316400] ; National Natural Science Foundation of China[61025011] ; National Natural Science Foundation of China[61202322] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61390511] ; National Natural Science Foundation of China[61303160] ; National Natural Science Foundation of China[61303153] ; 863 Program of China[2014AA015202] ; China Post-Doctoral Science Foundation[2013M530739] ; China Post-Doctoral Science Foundation[2012M520436]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000372547400011
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：30[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/8687
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Guorong
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100080, Peoples R China 3.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
推荐引用方式 GB/T 7714	Chu, Lingyang,Zhang, Yanyan,Li, Guorong,et al. Effective Multimodality Fusion Framework for Cross-Media Topic Detection[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2016,26(3):556-569.
APA	Chu, Lingyang,Zhang, Yanyan,Li, Guorong,Wang, Shuhui,Zhang, Weigang,&Huang, Qingming.(2016).Effective Multimodality Fusion Framework for Cross-Media Topic Detection.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,26(3),556-569.
MLA	Chu, Lingyang,et al."Effective Multimodality Fusion Framework for Cross-Media Topic Detection".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 26.3(2016):556-569.