Beyond global fusion: A group-aware fusion approach for multi-view image clustering

doi:10.1016/j.ins.2019.04.034

	Beyond global fusion: A group-aware fusion approach for multi-view image clustering
	Xue, Zhe 1,2; Li, Guorong 2,3; Wang, Shuhui 4; Huang, Jun 2,5; Zhang, Weigang 6; Huang, Qingming 2,3,4
	2019-08-01
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	493 页码:176-191
摘要	Images can be represented by multiple views and each view describes a specific visual appearance. Compared with single view learning method, multi-view methods can integrate information of different views to generate better clustering performance. Most of the existing multi-view methods assume that the importance of each view is the same to all the images. However, since visual appearance of images are different, the description abilities of different features vary with images. To solve this problem, a group-aware multi-view fusion approach is proposed in this paper. Specifically, images are partitioned into groups according to their visual appearance, and different fusion weights are assigned to different groups. We develop two paradigms under our group-aware fusion framework: pair-wise fusion and center-wise fusion. The former focuses on generating more accurate fusion results, while the latter achieves lower computational complexity. We design an optimization objective function which combines consensus and discrimination criterion to select more reliable and discriminative views for multi-view fusion. The clustering results and the fusion weights are learned by an iterative optimization algorithm. Experiments on four real-world image datasets indicate that our approach achieves promising image clustering performance over the existing methods. (C) 2019 Elsevier Inc. All rights reserved.
关键词	Multi-view learning Local fusion strategy Group-aware fusion Image clustering
DOI	10.1016/j.ins.2019.04.034
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61802028] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61772494] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61472389] ; National Natural Science Foundation of China[61532006] ; National Natural Science Foundation of China[61877006] ; National Natural Science Foundation of China[61772083] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013] ; Youth Innovation Promotion Association CAS ; Fundamental Research Funds for the Central University[2018RC44] ; Chinese Academy of Sciences ; Director Foundation of Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia[ITSM20180102]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000470052500011
出版者	ELSEVIER SCIENCE INC
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/2XEOYT63/4204
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.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China 2.Univ Chinese Acad Sci CAS, Sch Comp Sci & Technol, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Comput Tech, Key Lab Intell Info Proc, Beijing 100080, Peoples R China 5.Anhui Univ Technol, Sch Comp Sci & Technol, Maanshan, Peoples R China 6.Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264209, Peoples R China
推荐引用方式 GB/T 7714	Xue, Zhe,Li, Guorong,Wang, Shuhui,et al. Beyond global fusion: A group-aware fusion approach for multi-view image clustering[J]. INFORMATION SCIENCES,2019,493:176-191.
APA	Xue, Zhe,Li, Guorong,Wang, Shuhui,Huang, Jun,Zhang, Weigang,&Huang, Qingming.(2019).Beyond global fusion: A group-aware fusion approach for multi-view image clustering.INFORMATION SCIENCES,493,176-191.
MLA	Xue, Zhe,et al."Beyond global fusion: A group-aware fusion approach for multi-view image clustering".INFORMATION SCIENCES 493(2019):176-191.