A review of co-saliency detection algorithms: Fundamentals, applications, and challenges

doi:10.1145/3158674

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	A review of co-saliency detection algorithms: Fundamentals, applications, and challenges
	Zhang, Dingwen 1; Fu, Huazhu 2; Han, Jun Wei 1; Borji, Ali 3; Li, Xuelong 4
刊名	ACM Transactions on Intelligent Systems and Technology
	2018-01
卷号	9 期号:4
ISSN号	21576904
DOI	10.1145/3158674
产权排序	4
英文摘要	Co-saliency detection is a newly emerging and rapidly growing research area in the computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and it can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this article, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field and to give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms. © 2018 ACM.
语种	英语
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/30783]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.School of Automation, Northwestern Polytechnical University, Xi'an; 710072, China; 2.Ocular Imaging Department, Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore; 3.Center for Research in Computer Vision, University of Central Florida, Orlando, United States; 4.Center for OPTical IMagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
推荐引用方式 GB/T 7714	Zhang, Dingwen,Fu, Huazhu,Han, Jun Wei,et al. A review of co-saliency detection algorithms: Fundamentals, applications, and challenges[J]. ACM Transactions on Intelligent Systems and Technology,2018,9(4).
APA	Zhang, Dingwen,Fu, Huazhu,Han, Jun Wei,Borji, Ali,&Li, Xuelong.(2018).A review of co-saliency detection algorithms: Fundamentals, applications, and challenges.ACM Transactions on Intelligent Systems and Technology,9(4).
MLA	Zhang, Dingwen,et al."A review of co-saliency detection algorithms: Fundamentals, applications, and challenges".ACM Transactions on Intelligent Systems and Technology 9.4(2018).