Multi-layer group sparse coding - For concurrent image classification and annotation

Gao, S; Chia, LT; Tsang, IWH

Multi-layer group sparse coding - For concurrent image classification and annotation

Gao, S Chia, LT Tsang, IWH

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2809 - 2816
Issue Date:: 2011-01-01

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Field	Value	Language
dc.contributor.author	Gao, S	en_US
dc.contributor.author	Chia, LT	en_US
dc.contributor.author	Tsang, IWH https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2809 - 2816	en_US
dc.identifier.isbn	9781457703942	en_US
dc.identifier.issn	1063-6919	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120251
dc.description.abstract	We present a multi-layer group sparse coding framework for concurrent image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes the image content as a whole, and tags, which describe the components of the image content. Then we propose a multi-layer group based tag propagation method, which combines the class label and subgroups of instances with similar tag distribution to annotate test images. Moreover, we extend our multi-layer group sparse coding in the Reproducing Kernel Hilbert Space (RKHS) which captures the nonlinearity of features, and further improves performances of image classification and annotation. Experimental results on the LabelMe, UIUC-Sport and NUS-WIDE-Object databases show that our method outperforms the baseline methods, and achieves excellent performances in both image classification and annotation tasks. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/CVPR.2011.5995454	en_US
dc.title	Multi-layer group sparse coding - For concurrent image classification and annotation	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

We present a multi-layer group sparse coding framework for concurrent image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes the image content as a whole, and tags, which describe the components of the image content. Then we propose a multi-layer group based tag propagation method, which combines the class label and subgroups of instances with similar tag distribution to annotate test images. Moreover, we extend our multi-layer group sparse coding in the Reproducing Kernel Hilbert Space (RKHS) which captures the nonlinearity of features, and further improves performances of image classification and annotation. Experimental results on the LabelMe, UIUC-Sport and NUS-WIDE-Object databases show that our method outperforms the baseline methods, and achieves excellent performances in both image classification and annotation tasks. © 2011 IEEE.

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http://hdl.handle.net/10453/120251