UM
Status已發表Published
Affiliated with RCfalse
Linear Representation-Based Methods for Image Classification: A Survey
Zhou,Jianhang; Zeng,Shaoning; Zhang,Bob
2020-12
Source PublicationIEEE Access
ISSN2169-3536
Volume8Pages:216645-216670
Abstract

In recent years, linear representation-based methods have been widely researched and applied in the image classification field. Generally speaking, there are three steps within linear representation-based classification (LRC) algorithms. The first step is coding, which uses all training samples to represent the test sample in a linear combination. The second step is subspace approximation, where residuals between the test sample and the linear combination of each class are calculated. The third step is classification, which assigns the class label to the minimum class-specific residual. We classify the LRC methods into six categories: 1) linear representation-based classification methods with norm minimizations, 2) linear representation-based classification methods with constraints, 3) linear representation-based classification methods with feature spaces, 4) linear representation-based classification methods with structural information, 5) linear representation with subspace learning, and 6) linear representation in semi-supervised learning and unsupervised learning. The purpose of this paper is to: 1) make an accurate and clear definition of the linear representation-based method, 2) provide a categorization and a comprehensive survey of the existing linear representation-based classification methods for image classification, 3) Summarize the main applications of linear representation-based methods, 4) provide extensive classification results and a discussion of the linear representation-based methods. Furthermore, this paper summarizes specific applications of the linear representation-based methods. Particularly, we performed extensive experiments to compare thirteen linear representation-based classification methods on seven image classification datasets.

KeywordImage Classification Linear Representation Optimization
DOI10.1109/ACCESS.2020.3041154
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000597792100001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA
Scopus ID2-s2.0-85097413817
Fulltext Access
Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhang,Bob
AffiliationDepartment of Computer and Information Science,Pami Research Group,Faculty of Science and Technology,University of Macau,Taipa,Macao
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Zhou,Jianhang,Zeng,Shaoning,Zhang,Bob. Linear Representation-Based Methods for Image Classification: A Survey[J]. IEEE Access,2020,8:216645-216670.
APA Zhou,Jianhang,Zeng,Shaoning,&Zhang,Bob.(2020).Linear Representation-Based Methods for Image Classification: A Survey.IEEE Access,8,216645-216670.
MLA Zhou,Jianhang,et al."Linear Representation-Based Methods for Image Classification: A Survey".IEEE Access 8(2020):216645-216670.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhou,Jianhang]'s Articles
[Zeng,Shaoning]'s Articles
[Zhang,Bob]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhou,Jianhang]'s Articles
[Zeng,Shaoning]'s Articles
[Zhang,Bob]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhou,Jianhang]'s Articles
[Zeng,Shaoning]'s Articles
[Zhang,Bob]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

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