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Effective Multimodality Fusion Framework for Cross-Media Topic Detection

文献类型：期刊论文

作者Chu, Lingyang1; Zhang, Yanyan2; Li, Guorong2; Wang, Shuhui1; Zhang, Weigang3; Huang, Qingming1
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2016-03-01
卷号26期号:3页码:556-569
ISSN号1051-8215
关键词Cross-media fusion multimodality topic detection topic recovery (TR) We-Media
DOI10.1109/TCSVT.2014.2347551
英文摘要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.
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
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000372547400011
源URL[http://119.78.100.204/handle/2XEOYT63/8686]  
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.

入库方式： OAI收割

来源：计算技术研究所

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