Group sparse Multiview patch alignment framework with view consistency for image classification
Jie Gui1,2; Dacheng Tao3; Zhenan Sun2; Yong Luo4; Xinge You5; Yuan Yan Tang6
Source PublicationIEEE Transactions on Image Processing
Volume23Issue:7Pages:3126 - 3137

No single feature can satisfactorily characterize the semantic concepts of an image. Multiview learning aims to unify different kinds of features to produce a consensual and efficient representation. This paper redefines part optimization in the patch alignment framework (PAF) and develops a group sparse multiview patch alignment framework (GSM-PAF). The new part optimization considers not only the complementary properties of different views, but also view consistency. In particular, view consistency models the correlations between all possible combinations of any two kinds of view. In contrast to conventional dimensionality reduction algorithms that perform feature extraction and feature selection independently, GSM-PAF enjoys joint feature extraction and feature selection by exploiting I2,1 -norm on the projection matrix to achieve row sparsity, which leads to the simultaneous selection of relevant features and learning transformation, and thus makes the algorithm more discriminative. Experiments on two real-world image data sets demonstrate the effectiveness of GSM-PAF for image classification. 

KeywordGroup Sparse Multiview Learning Patch Alignment Framework View Consistency Joint Feature Extraction And Feature Selection
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000337842700010
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:73   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorJie Gui; Dacheng Tao; Zhenan Sun; Yong Luo; Xinge You; Yuan Yan Tang
Affiliation1.Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
2.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3.Centre for Quantum Computation and Intelligent Systems, Faculty of Engineering and Information Technology, University of Technology, Sydney, Ultimo, NSW 2007, Australia
4.Key Laboratory of Machine Perception, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
5.Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
6.Department of Computer and Information Science, University of Macau, Macau, China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jie Gui,Dacheng Tao,Zhenan Sun,et al. Group sparse Multiview patch alignment framework with view consistency for image classification[J]. IEEE Transactions on Image Processing,2014,23(7):3126 - 3137.
APA Jie Gui,Dacheng Tao,Zhenan Sun,Yong Luo,Xinge You,&Yuan Yan Tang.(2014).Group sparse Multiview patch alignment framework with view consistency for image classification.IEEE Transactions on Image Processing,23(7),3126 - 3137.
MLA Jie Gui,et al."Group sparse Multiview patch alignment framework with view consistency for image classification".IEEE Transactions on Image Processing 23.7(2014):3126 - 3137.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jie Gui]'s Articles
[Dacheng Tao]'s Articles
[Zhenan Sun]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jie Gui]'s Articles
[Dacheng Tao]'s Articles
[Zhenan Sun]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jie Gui]'s Articles
[Dacheng Tao]'s Articles
[Zhenan Sun]'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.