Explanation guided cross-modal social image clustering

doi:10.1016/j.ins.2022.01.065

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Explanation guided cross-modal social image clustering
	Yan, Xiaoqiang 3; Mao, Yiqiao 3; Ye, Yangdong 3; Yu, Hui 4; Wang, Fei-Yue 1,2,5
刊名	INFORMATION SCIENCES
	2022-05-01
卷号	593 页码:1-16
关键词	Social image clustering Human explanation Side information Information maximization Interactive optimization
ISSN号	0020-0255
DOI	10.1016/j.ins.2022.01.065
通讯作者	Ye, Yangdong(ieydye@zzu.edu.cn)
英文摘要	The integration of visual and semantic information has been found to play a role in increasing the accuracy of social image clustering methods. However, existing approaches are limited by the heterogeneity gap between the visual and semantic modalities, and their performances significantly degrade due to the commonly sparse and incomplete tags in semantic modality. To address these problems, we propose a novel clustering framework to discover reasonable categories in unlabeled social images under the guidance of human explanations. First of all, a novel Explanation Generation Model (EGM) is proposed to automatically boost textual information for the sparse and incomplete tags based on an extra lexical database with human knowledge. Then, a novel clustering algorithm called Group Constrained Information Maximization (GCIM) is proposed to learn image categories. In this algorithm, a new type of constraint named group level side information is unprecedentedly defined to bridge the well-known heterogeneity gap between visual and textual modalities. Finally, an interactive draw-and-merge optimization method is proposed to ensure an optimal solution. Extensive experiments on several social image datasets including NUS-Wide, IAPRTC, MIRFlickr, ESP-Game and COCO demonstrate the superiority of the proposed approach to state-of-the-art baselines. (c) 2022 Elsevier Inc. All rights reserved.
资助项目	National Natural Science Foundation of China[61906172] ; National Natural Science Foundation of China[62176239] ; Postdoctoral Research Foundation of China[2020M682357] ; EPSRC through project 4D Facial Sensing and Modelling[EP/N025849/1]
WOS关键词	INFORMATION BOTTLENECK ; MULTIVIEW
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000770686400001
资助机构	National Natural Science Foundation of China ; Postdoctoral Research Foundation of China ; EPSRC through project 4D Facial Sensing and Modelling
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48228]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Ye, Yangdong
作者单位	1.Macau Univ Sci & Technol, Inst Syst Engn, Taipa 999078, Macao, Peoples R China 2.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 3.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450052, Peoples R China 4.Univ Portsmouth, Sch Creat Technol, Portsmouth PO1 2DJ, Hants, England 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yan, Xiaoqiang,Mao, Yiqiao,Ye, Yangdong,et al. Explanation guided cross-modal social image clustering[J]. INFORMATION SCIENCES,2022,593:1-16.
APA	Yan, Xiaoqiang,Mao, Yiqiao,Ye, Yangdong,Yu, Hui,&Wang, Fei-Yue.(2022).Explanation guided cross-modal social image clustering.INFORMATION SCIENCES,593,1-16.
MLA	Yan, Xiaoqiang,et al."Explanation guided cross-modal social image clustering".INFORMATION SCIENCES 593(2022):1-16.