Shared feature extraction for semi-supervised image classification

Luo, Y; Tao, D; Geng, B; Xu, C; Maybank, S

Shared feature extraction for semi-supervised image classification

Luo, Y Tao, D

Geng, B Xu, C Maybank, S

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Publication Type:: Conference Proceeding
Citation:: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 1165 - 1168
Issue Date:: 2011-12-29

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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	Geng, B	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Maybank, S	en_US
dc.date.issued	2011-12-29	en_US
dc.identifier.citation	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops, 2011, pp. 1165 - 1168	en_US
dc.identifier.isbn	9781450306164	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32344
dc.description.abstract	Multi-task learning (MTL) plays an important role in image analysis applications, e.g. image classification, face recognition and image annotation. That is because MTL can estimate the latent shared subspace to represent the common features given a set of images from different tasks. However, the geometry of the data probability distribution is always supported on an intrinsic image sub-manifold that is embedded in a high dimensional Euclidean space. Therefore, it is improper to directly apply MTL to multiclass image classification. In this paper, we propose a manifold regularized MTL (MRMTL) algorithm to discover the latent shared subspace by treating the high-dimensional image space as a submanifold embedded in an ambient space. We conduct experiments on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes by comparing MRMTL with conventional MTL and several representative image classification algorithms. The results suggest that MRMTL can properly extract the common features for image representation and thus improve the generalization performance of the image classification models. Copyright 2011 ACM.	en_US
dc.relation.ispartof	MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops	en_US
dc.relation.isbasedon	10.1145/2072298.2071965	en_US
dc.title	Shared feature extraction for semi-supervised 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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Multi-task learning (MTL) plays an important role in image analysis applications, e.g. image classification, face recognition and image annotation. That is because MTL can estimate the latent shared subspace to represent the common features given a set of images from different tasks. However, the geometry of the data probability distribution is always supported on an intrinsic image sub-manifold that is embedded in a high dimensional Euclidean space. Therefore, it is improper to directly apply MTL to multiclass image classification. In this paper, we propose a manifold regularized MTL (MRMTL) algorithm to discover the latent shared subspace by treating the high-dimensional image space as a submanifold embedded in an ambient space. We conduct experiments on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes by comparing MRMTL with conventional MTL and several representative image classification algorithms. The results suggest that MRMTL can properly extract the common features for image representation and thus improve the generalization performance of the image classification models. Copyright 2011 ACM.

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