Semisupervised feature selection via spline regression for video semantic recognition

Han, Y; Yang, Y; Yan, Y; Ma, Z; Sebe, N; Zhou, X

Semisupervised feature selection via spline regression for video semantic recognition

Han, Y Yang, Y

Yan, Y Ma, Z Sebe, N Zhou, X

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (2), pp. 252 - 264
Issue Date:: 2015-02-01

Closed Access

	Filename	Description	Size
	06786497.pdf	Published Version	3.75 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Han, Y	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Yan, Y	en_US
dc.contributor.author	Ma, Z	en_US
dc.contributor.author	Sebe, N	en_US
dc.contributor.author	Zhou, X	en_US
dc.date.issued	2015-02-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (2), pp. 252 - 264	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121577
dc.description.abstract	© 2014 IEEE. To improve both the efficiency and accuracy of video semantic recognition, we can perform feature selection on the extracted video features to select a subset of features from the high-dimensional feature set for a compact and accurate video data representation. Provided the number of labeled videos is small, supervised feature selection could fail to identify the relevant features that are discriminative to target classes. In many applications, abundant unlabeled videos are easily accessible. This motivates us to develop semisupervised feature selection algorithms to better identify the relevant video features, which are discriminative to target classes by effectively exploiting the information underlying the huge amount of unlabeled video data. In this paper, we propose a framework of video semantic recognition by semisupervised feature selection via spline regression (S2FS2R). Two scatter matrices are combined to capture both the discriminative information and the local geometry structure of labeled and unlabeled training videos: A within-class scatter matrix encoding discriminative information of labeled training videos and a spline scatter output from a local spline regression encoding data distribution. An ℓ2,1-norm is imposed as a regularization term on the transformation matrix to ensure it is sparse in rows, making it particularly suitable for feature selection. To efficiently solve S2FS2R, we develop an iterative algorithm and prove its convergency. In the experiments, three typical tasks of video semantic recognition, such as video concept detection, video classification, and human action recognition, are used to demonstrate that the proposed S2FS2R achieves better performance compared with the state-of-the-art methods.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DE130101311
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2014.2314123	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Semisupervised feature selection via spline regression for video semantic recognition	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	26	en_US
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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2014 IEEE. To improve both the efficiency and accuracy of video semantic recognition, we can perform feature selection on the extracted video features to select a subset of features from the high-dimensional feature set for a compact and accurate video data representation. Provided the number of labeled videos is small, supervised feature selection could fail to identify the relevant features that are discriminative to target classes. In many applications, abundant unlabeled videos are easily accessible. This motivates us to develop semisupervised feature selection algorithms to better identify the relevant video features, which are discriminative to target classes by effectively exploiting the information underlying the huge amount of unlabeled video data. In this paper, we propose a framework of video semantic recognition by semisupervised feature selection via spline regression (S2FS2R). Two scatter matrices are combined to capture both the discriminative information and the local geometry structure of labeled and unlabeled training videos: A within-class scatter matrix encoding discriminative information of labeled training videos and a spline scatter output from a local spline regression encoding data distribution. An ℓ2,1-norm is imposed as a regularization term on the transformation matrix to ensure it is sparse in rows, making it particularly suitable for feature selection. To efficiently solve S2FS2R, we develop an iterative algorithm and prove its convergency. In the experiments, three typical tasks of video semantic recognition, such as video concept detection, video classification, and human action recognition, are used to demonstrate that the proposed S2FS2R achieves better performance compared with the state-of-the-art methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121577