Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval

doi:10.1109/TNNLS.2020.2984676

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 > 机器人应用与理论组

	Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval
	Liu, Shenglan 1,2; Sun, Muxin 4; Feng, Lin 1,2; Qiao, Hong 3; Chen, Shuyuan 1,2; Liu, Yang 1,2
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021-03-01
卷号	32 期号:3 页码:1389-1399
关键词	Image retrieval multigraph fusion reranking three degrees of influence
ISSN号	2162-237X
DOI	10.1109/TNNLS.2020.2984676
通讯作者	Liu, Yang(ly@dlut.edu.cn)
英文摘要	A single feature is hard to describe the content of images from an overall perspective, which limits the retrieval performances of single-feature-based methods in image retrieval tasks. To fully describe the properties of images and improve the retrieval performances, multifeature fusion ranking-based methods are proposed. However, the effectiveness of multifeature fusion in image retrieval has not been theoretically explained. This article gives a theoretical proof to illustrate the role of independent features in improving the retrieval results. Based on the theoretical proof, the original ranking list generated with a single feature greatly influences the performances of multifeature fusion ranking. Inspired by the principle of three degrees of influence in social networks, this article proposes a reranking method named k-nearest neighbors' neighbors' neighbors' graph (N3G) to improve the original ranking list by a single feature. Furthermore, a multigraph fusion ranking (MFR) method motivated by the group relation theory in social networks for multifeature ranking is also proposed, which considers the correlations of all images in multiple neighborhood graphs. Evaluation experiments conducted on several representative data sets (e.g., UK-bench, Holiday, Corel-10K, and Cifar-10) validate that N3G and MFR outperform the other state-of-the-art methods.
资助项目	National Natural Science Foundation of China[61602082] ; National Natural Science Foundation of China[61672130] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61972064] ; National Key Scientific Instrument and Equipment Development Project[61627808] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001] ; Liaoning Revitalization Talents Program[XLYC1806006] ; Fundamental Research Funds for the Central Universities of China[DUT19RC(3)012]
WOS关键词	OBJECT RETRIEVAL
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000626332700036
资助机构	National Natural Science Foundation of China ; National Key Scientific Instrument and Equipment Development Project ; Development of Science and Technology of Guangdong Province Special Fund Project ; Liaoning Revitalization Talents Program ; Fundamental Research Funds for the Central Universities of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44082]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者	Liu, Yang
作者单位	1.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dalian 116024, Peoples R China 2.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian 116024, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Qianxun Spatial Intelligence Inc, Shanghai 200438, Peoples R China
推荐引用方式 GB/T 7714	Liu, Shenglan,Sun, Muxin,Feng, Lin,et al. Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(3):1389-1399.
APA	Liu, Shenglan,Sun, Muxin,Feng, Lin,Qiao, Hong,Chen, Shuyuan,&Liu, Yang.(2021).Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(3),1389-1399.
MLA	Liu, Shenglan,et al."Social Neighborhood Graph and Multigraph Fusion Ranking for Multifeature Image Retrieval".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.3(2021):1389-1399.