Recognition of Emotions in User-Generated Videos with Kernelized Features

Zhang, H; Xu, M

Recognition of Emotions in User-Generated Videos with Kernelized Features

Zhang, H

Xu, M

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Multimedia, 2018, 20 (10), pp. 2824 - 2835
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Zhang, H https://orcid.org/0000-0002-0021-3634	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	IEEE Transactions on Multimedia, 2018, 20 (10), pp. 2824 - 2835	en_US
dc.identifier.issn	1520-9210	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132593
dc.description.abstract	© 1999-2012 IEEE. Recognition of emotions in user-generated videos has attracted increasing research attention. Most existing approaches are based on spatial features extracted from video frames. However, due to the broad affective gap between spatial features of images and high-level emotions, the performance of existing approaches is restricted. To bridge the affective gap, we propose recognizing emotions in user-generated videos with kernelized features. We reformulate the equation of the discrete Fourier transform as a linear kernel function and construct a polynomial kernel function based on the linear kernel. The polynomial kernel is applied to spatial features of video frames to generate kernelized features. Compared with spatial features, kernelized features show superior discriminative capability. Moreover, we are the first to apply the sparse representation method to reduce the impact of noise contained in videos; this method helps contribute to performance improvement. Extensive experiments are conducted on two challenging benchmark datasets, that is, VideoEmotion-8 and Ekman-6. The experimental results demonstrate that the proposed method achieves state-of-the-art performance.	en_US
dc.relation.ispartof	IEEE Transactions on Multimedia	en_US
dc.relation.isbasedon	10.1109/TMM.2018.2808760	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Recognition of Emotions in User-Generated Videos with Kernelized Features	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	20	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

© 1999-2012 IEEE. Recognition of emotions in user-generated videos has attracted increasing research attention. Most existing approaches are based on spatial features extracted from video frames. However, due to the broad affective gap between spatial features of images and high-level emotions, the performance of existing approaches is restricted. To bridge the affective gap, we propose recognizing emotions in user-generated videos with kernelized features. We reformulate the equation of the discrete Fourier transform as a linear kernel function and construct a polynomial kernel function based on the linear kernel. The polynomial kernel is applied to spatial features of video frames to generate kernelized features. Compared with spatial features, kernelized features show superior discriminative capability. Moreover, we are the first to apply the sparse representation method to reduce the impact of noise contained in videos; this method helps contribute to performance improvement. Extensive experiments are conducted on two challenging benchmark datasets, that is, VideoEmotion-8 and Ekman-6. The experimental results demonstrate that the proposed method achieves state-of-the-art performance.

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http://hdl.handle.net/10453/132593