Multi-View Multi-Label Learning with Sparse Feature Selection for Image Annotation

Zhang, Y; Wu, J; Cai, Z; Yu, PS

Multi-View Multi-Label Learning with Sparse Feature Selection for Image Annotation

Zhang, Y Wu, J Cai, Z Yu, PS

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Multimedia, 2020, 22, (11), pp. 2844-2857
Issue Date:: 2020-11-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Wu, J
dc.contributor.author	Cai, Z
dc.contributor.author	Yu, PS
dc.date.accessioned	2021-03-14T06:17:54Z
dc.date.available	2021-03-14T06:17:54Z
dc.date.issued	2020-11-01
dc.identifier.citation	IEEE Transactions on Multimedia, 2020, 22, (11), pp. 2844-2857
dc.identifier.issn	1520-9210
dc.identifier.issn	1941-0077
dc.identifier.uri	http://hdl.handle.net/10453/147099
dc.description.abstract	© 1999-2012 IEEE. In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However, multiple view data may include noisy, irrelevant and redundant features, while multiple class labels can be noisy and incomplete. Due to the special data characteristic, it is hard to perform feature selection on multi-view multi-label data. To address these challenges, in this paper, we propose a novel multi-view multi-label sparse feature selection (MSFS) method, which exploits both view relations and label correlations to select discriminative features for further learning. Specifically, the multi-labeled information is decomposed into a reduced latent label representation to capture higher level concepts and correlations among multiple labels. Multiple local geometric structures are constructed to exploit visual similarities and relations for different views. By taking full advantage of the latent label representation and multiple local geometric structures, the sparse regression model with an l_{2,1}-norm and an Frobenius norm (F-norm) penalty terms is utilized to perform hierarchical feature selection, where the F-norm penalty performs high-level (i.e., view-wise) feature selection to preserve the informative views and the l_{2,1}-norm penalty conducts low-level (i.e., row-wise) feature selection to remove noisy features. To solve the proposed formulation, we also devise a simple yet efficient iterative algorithm. Experiments and comparisons on real-world image datasets demonstrate the effectiveness and potential of MSFS.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Multimedia
dc.relation.isbasedon	10.1109/TMM.2020.2966887
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Multi-View Multi-Label Learning with Sparse Feature Selection for Image Annotation
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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	*
dc.date.updated	2021-03-14T06:17:49Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	11

Abstract:

© 1999-2012 IEEE. In image analysis, image samples are always represented by multiple view features and associated with multiple class labels for better interpretation. However, multiple view data may include noisy, irrelevant and redundant features, while multiple class labels can be noisy and incomplete. Due to the special data characteristic, it is hard to perform feature selection on multi-view multi-label data. To address these challenges, in this paper, we propose a novel multi-view multi-label sparse feature selection (MSFS) method, which exploits both view relations and label correlations to select discriminative features for further learning. Specifically, the multi-labeled information is decomposed into a reduced latent label representation to capture higher level concepts and correlations among multiple labels. Multiple local geometric structures are constructed to exploit visual similarities and relations for different views. By taking full advantage of the latent label representation and multiple local geometric structures, the sparse regression model with an l_{2,1}-norm and an Frobenius norm (F-norm) penalty terms is utilized to perform hierarchical feature selection, where the F-norm penalty performs high-level (i.e., view-wise) feature selection to preserve the informative views and the l_{2,1}-norm penalty conducts low-level (i.e., row-wise) feature selection to remove noisy features. To solve the proposed formulation, we also devise a simple yet efficient iterative algorithm. Experiments and comparisons on real-world image datasets demonstrate the effectiveness and potential of MSFS.

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http://hdl.handle.net/10453/147099