Thinking of images as what they are: Compound matrix regression for image classification

Ma, Z; Yang, Y; Nie, F; Sebe, N

Thinking of images as what they are: Compound matrix regression for image classification

Ma, Z Yang, Y

Nie, F Sebe, N

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Publication Type:: Conference Proceeding
Citation:: IJCAI International Joint Conference on Artificial Intelligence, 2013, pp. 1530 - 1536
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Ma, Z	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Nie, F	en_US
dc.contributor.author	Sebe, N	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	IJCAI International Joint Conference on Artificial Intelligence, 2013, pp. 1530 - 1536	en_US
dc.identifier.isbn	9781577356332	en_US
dc.identifier.issn	1045-0823	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119953
dc.description.abstract	In this paper, we propose a new classification framework for image matrices. The approach is realized by learning two groups of classification vectors for each dimension of the image matrices. One novelty is that we utilize compound regression models in the learning process, which endows the algorithm increased degree of freedom. On top of that, we extend the two-dimensional classification method to a semi-supervised classifier which leverages both labeled and unlabeled data. A fast iterative solution is then proposed to solve the objective function. The proposed method is evaluated by several different applications. The experimental results show that our method outperforms several classification approaches. In addition, we observe that our method attains respectable classification performance even when only few labeled training samples are provided. This advantage is especially desirable for real-world problems since precisely annotated images are scarce.	en_US
dc.relation.ispartof	IJCAI International Joint Conference on Artificial Intelligence	en_US
dc.title	Thinking of images as what they are: Compound matrix regression for image classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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:

In this paper, we propose a new classification framework for image matrices. The approach is realized by learning two groups of classification vectors for each dimension of the image matrices. One novelty is that we utilize compound regression models in the learning process, which endows the algorithm increased degree of freedom. On top of that, we extend the two-dimensional classification method to a semi-supervised classifier which leverages both labeled and unlabeled data. A fast iterative solution is then proposed to solve the objective function. The proposed method is evaluated by several different applications. The experimental results show that our method outperforms several classification approaches. In addition, we observe that our method attains respectable classification performance even when only few labeled training samples are provided. This advantage is especially desirable for real-world problems since precisely annotated images are scarce.

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http://hdl.handle.net/10453/119953