Mixed-norm regression for visual classification

Zhu, X; Zhang, J; Zhang, S

Mixed-norm regression for visual classification

Zhu, X Zhang, J Zhang, S

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Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8346 LNAI (PART 1), pp. 265 - 276
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8346 LNAI (PART 1), pp. 265 - 276	en_US
dc.identifier.isbn	9783642539138	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28319
dc.description.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. © Springer-Verlag 2013.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-53914-5_23	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Mixed-norm regression for visual classification	en_US
dc.type	Journal Article
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	8346 LNAI	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	8346 LNAI	en_US

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. © Springer-Verlag 2013.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/28319