Vector-valued multi-view semi-supervised learning for multi-label image classification

Luo, Y; Tao, D; Xu, C; Li, D

Vector-valued multi-view semi-supervised learning for multi-label image classification

Luo, Y Tao, D

Xu, C

Li, D

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013, 2013, pp. 647 - 653
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Luo, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Xu, C https://orcid.org/0000-0002-4756-0609	en_US
dc.contributor.author	Li, D	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013, 2013, pp. 647 - 653	en_US
dc.identifier.isbn	9781577356158	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28341
dc.description.abstract	Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e.g. a pedestrian, bicycle and tree) and multiple visual features (e.g. color, texture and shape). Currently available tools tend to use either labels or features for classification, but both are necessary to describe the image properly. There have been recent successes in using vector-valued functions, which construct matrix-valued kernels, to explore the multi-label structure in the output space. This has motivated us to develop multi-view vector-valued manifold regularization (MV3MR) in order to integrate multiple features. MV3MR exploits the complementary properties of different features, and discovers the intrinsic local geometry of the compact support shared by different features, under the theme of manifold regularization. We validate the effectiveness of the proposed MV3 MR methodology for image classification by conducting extensive experiments on two challenge datasets, PASCAL VOC' 07 and MIR Flickr. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.	en_US
dc.relation.ispartof	Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013	en_US
dc.title	Vector-valued multi-view semi-supervised learning for multi-label image classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Bellevue, Washington, USA	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.publication-status	Published	en_US

Abstract:

Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e.g. a pedestrian, bicycle and tree) and multiple visual features (e.g. color, texture and shape). Currently available tools tend to use either labels or features for classification, but both are necessary to describe the image properly. There have been recent successes in using vector-valued functions, which construct matrix-valued kernels, to explore the multi-label structure in the output space. This has motivated us to develop multi-view vector-valued manifold regularization (MV3MR) in order to integrate multiple features. MV3MR exploits the complementary properties of different features, and discovers the intrinsic local geometry of the compact support shared by different features, under the theme of manifold regularization. We validate the effectiveness of the proposed MV3 MR methodology for image classification by conducting extensive experiments on two challenge datasets, PASCAL VOC' 07 and MIR Flickr. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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http://hdl.handle.net/10453/28341