OPT OpenIR  > 光学影像学习与分析中心
A review of co-saliency detection algorithms: Fundamentals, applications, and challenges
Zhang, Dingwen1; Fu, Huazhu2; Han, Jun Wei1; Borji, Ali3; Li, Xuelong4
Department光学影像学习与分析中心
2018-01
Source PublicationACM Transactions on Intelligent Systems and Technology
ISSN21576904
Volume9Issue:4
Contribution Rank4
Abstract

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.

 

DOI10.1145/3158674
Indexed BySCI ; EI
Language英语
EI Accession Number20180604769414
Citation statistics
Cited Times:17[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30783
Collection光学影像学习与分析中心
Affiliation1.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
Recommended Citation
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).
Files in This Item:
File Name/Size DocType Version Access License
A review of co-salie(1845KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Dingwen]'s Articles
[Fu, Huazhu]'s Articles
[Han, Jun Wei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Dingwen]'s Articles
[Fu, Huazhu]'s Articles
[Han, Jun Wei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Dingwen]'s Articles
[Fu, Huazhu]'s Articles
[Han, Jun Wei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: A review of co-saliency detection algorithms Fundamentals, applications, and challenges.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.