Fusing cross-media for topic detection by dense keyword groups

doi:10.1016/j.neucom.2015.02.083

	Fusing cross-media for topic detection by dense keyword groups
	Zhang, Weigang 1; Chen, Tianlong 2; Li, Guorong 3; Pang, Junbiao 4; Huang, Qingming 2,3; Gao, Wen 1,5
	2015-12-02
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	169 页码:169-179
摘要	Events are real-world occurrences that lead to the explosive growth of web multimedia content such as images, videos and texts. Efficient organization and navigation of multimedia data in the topic level can boost users' understanding and enhance their experience of the events that have happened. Due to the potential application prospects, multimedia topic detection has been an active area of research with notable progress in the last decade. Traditional methods mainly focus on single media, so the results only reflect the characteristics of one certain media and topic browsing was not comprehensive enough. In this paper, we propose a method of utilizing and fusing rich media information from web videos and news reports to extract weighted keyword groups, which are used for cross-media topic detection. Firstly by utilizing the video-related textual information and the titles of news articles, a maximum local average score is proposed to find coarse weighted dense keyword groups; after that, textual linking and visual linking are applied to refine the keyword groups and update the weights; finally, the documents are re-linked with the refined keyword groups to form an event-related document set. Experiments are conducted on cross-media datasets containing web videos and news reports. The web videos are from Youku, YouTube's equivalent in China, the news reports from sina.com, some of which contain topic-related images. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.
关键词	Topic detection Cross-media Dense keyword group Near-duplicate keyframe Web video
DOI	10.1016/j.neucom.2015.02.083
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000359170300020
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：8[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/2XEOYT63/9541
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.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 4.Beijing Univ Technol, Coll Metropolitan Transportat, Beijing 100124, Peoples R China 5.Peking Univ, Inst Digital Media, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Weigang,Chen, Tianlong,Li, Guorong,et al. Fusing cross-media for topic detection by dense keyword groups[J]. NEUROCOMPUTING,2015,169:169-179.
APA	Zhang, Weigang,Chen, Tianlong,Li, Guorong,Pang, Junbiao,Huang, Qingming,&Gao, Wen.(2015).Fusing cross-media for topic detection by dense keyword groups.NEUROCOMPUTING,169,169-179.
MLA	Zhang, Weigang,et al."Fusing cross-media for topic detection by dense keyword groups".NEUROCOMPUTING 169(2015):169-179.