High-order distance-based multiview stochastic learning in image classification

Yu, J; Rui, Y; Tang, YY; Tao, D

High-order distance-based multiview stochastic learning in image classification

Yu, J Rui, Y Tang, YY Tao, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Cybernetics, 2014, 44 (12), pp. 2431 - 2442
Issue Date:: 2014-12-01

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Field	Value	Language
dc.contributor.author	Yu, J	en_US
dc.contributor.author	Rui, Y	en_US
dc.contributor.author	Tang, YY	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2014-12-01	en_US
dc.identifier.citation	IEEE Transactions on Cybernetics, 2014, 44 (12), pp. 2431 - 2442	en_US
dc.identifier.issn	2168-2267	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122392
dc.description.abstract	© 2014 IEEE. How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification.	en_US
dc.relation.ispartof	IEEE Transactions on Cybernetics	en_US
dc.relation.isbasedon	10.1109/TCYB.2014.2307862	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	High-order distance-based multiview stochastic learning in image classification	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	44	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	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	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	44	en_US

Abstract:

© 2014 IEEE. How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification.

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http://hdl.handle.net/10453/122392