Improving Visual Saliency Computing with Emotion Intensity

Liu, H; Xu, M; Wang, J; Rao, T; Burnett, I

Improving Visual Saliency Computing with Emotion Intensity

Liu, H Xu, M

Wang, J Rao, T Burnett, I

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Networks and Learning Systems, 2016, 27 (6), pp. 1201 - 1213
Issue Date:: 2016-06-01

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Field	Value	Language
dc.contributor.author	Liu, H	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Rao, T	en_US
dc.contributor.author	Burnett, I https://orcid.org/0000-0003-3795-7722	en_US
dc.date.issued	2016-06-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Networks and Learning Systems, 2016, 27 (6), pp. 1201 - 1213	en_US
dc.identifier.issn	2162-237X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121516
dc.description.abstract	© 2012 IEEE. Saliency maps that integrate individual feature maps into a global measure of visual attention are widely used to estimate human gaze density. Most of the existing methods consider low-level visual features and locations of objects, and/or emphasize the spatial position with center prior. Recent psychology research suggests that emotions strongly influence human visual attention. In this paper, we explore the influence of emotional content on visual attention. On top of the traditional bottom-up saliency map generation, our saliency map is generated in cooperation with three emotion factors, i.e., general emotional content, facial expression intensity, and emotional object locations. Experiments, carried out on National University of Singapore Eye Fixation (a public eye tracking data set), demonstrate that incorporating emotion does improve the quality of visual saliency maps computed by bottom-up approaches for the gaze density estimation. Our method increases about 0.1 on an average of area under the curve of receiver operation characteristic curve, compared with the four baseline bottom-up approaches (Itti's, attention based on information maximization, saliency using natural, and graph-based vision saliency).	en_US
dc.relation.ispartof	IEEE Transactions on Neural Networks and Learning Systems	en_US
dc.relation.isbasedon	10.1109/TNNLS.2016.2553579	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Improving Visual Saliency Computing with Emotion Intensity	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	27	en_US
utslib.for	080106 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/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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

© 2012 IEEE. Saliency maps that integrate individual feature maps into a global measure of visual attention are widely used to estimate human gaze density. Most of the existing methods consider low-level visual features and locations of objects, and/or emphasize the spatial position with center prior. Recent psychology research suggests that emotions strongly influence human visual attention. In this paper, we explore the influence of emotional content on visual attention. On top of the traditional bottom-up saliency map generation, our saliency map is generated in cooperation with three emotion factors, i.e., general emotional content, facial expression intensity, and emotional object locations. Experiments, carried out on National University of Singapore Eye Fixation (a public eye tracking data set), demonstrate that incorporating emotion does improve the quality of visual saliency maps computed by bottom-up approaches for the gaze density estimation. Our method increases about 0.1 on an average of area under the curve of receiver operation characteristic curve, compared with the four baseline bottom-up approaches (Itti's, attention based on information maximization, saliency using natural, and graph-based vision saliency).

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http://hdl.handle.net/10453/121516