Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

Handle URI:
http://hdl.handle.net/10754/556097
Title:
Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification
Authors:
Zhu, Xiaofeng; Xie, Qing ( 0000-0003-4530-588X ) ; Zhu, Yonghua; Liu, Xingyi; Zhang, Shichao
Abstract:
This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification 2015 Neurocomputing
Journal:
Neurocomputing
Issue Date:
28-May-2015
DOI:
10.1016/j.neucom.2014.08.106
Type:
Article
ISSN:
09252312
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0925231215006852
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhu, Xiaofengen
dc.contributor.authorXie, Qingen
dc.contributor.authorZhu, Yonghuaen
dc.contributor.authorLiu, Xingyien
dc.contributor.authorZhang, Shichaoen
dc.date.accessioned2015-05-31T08:53:01Zen
dc.date.available2015-05-31T08:53:01Zen
dc.date.issued2015-05-28en
dc.identifier.citationMulti-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification 2015 Neurocomputingen
dc.identifier.issn09252312en
dc.identifier.doi10.1016/j.neucom.2014.08.106en
dc.identifier.urihttp://hdl.handle.net/10754/556097en
dc.description.abstractThis paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.en
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0925231215006852en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 28 May 2015. DOI: 10.1016/j.neucom.2014.08.106en
dc.subjectImage classificationen
dc.subjectMulti-view classificationen
dc.subjectSparse codingen
dc.subjectStructure sparsityen
dc.subjectReproducing kernel Hilbert spaceen
dc.titleMulti-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classificationen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNeurocomputingen
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Mathematics and Statistics, Xi'an Jiaotong University, P. R. Chinaen
dc.contributor.institutionGuangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, P. R. Chinaen
dc.contributor.institutionSchool of Computer, Electronics and Information, Guangxi University, Chinaen
dc.contributor.institutionQinzhou Institute of Socialism, Qinzhou, Guangxi, Chinaen
kaust.authorXie, Qingen
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