Deep feature guided image retargeting

Wu, J; Xie, R; Song, L; Liu, B

Deep feature guided image retargeting

Wu, J Xie, R Song, L Liu, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019, 2020, 00, pp. 1-4
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Wu, J
dc.contributor.author	Xie, R
dc.contributor.author	Song, L
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.date	2019-12-01
dc.date.accessioned	2021-02-09T01:16:53Z
dc.date.available	2021-02-09T01:16:53Z
dc.date.issued	2020
dc.identifier.citation	2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019, 2020, 00, pp. 1-4
dc.identifier.isbn	9781728137230
dc.identifier.uri	http://hdl.handle.net/10453/145969
dc.description.abstract	© 2019 IEEE. Image retargeting is the technique to display images via devices with various aspect ratios and sizes. Traditional content-Aware retargeting methods rely on low-level features to predict pixel-wise importance and can hardly preserve both the structure lines and salient regions of the source image. To address this problem, we propose a novel adaptive image warping approach which integrates with deep convolutional neural network. In the proposed method, a visual importance map and a foreground mask map are generated by a pre-Trained network. The two maps and other constraints guide the warping process to yield retargeted results with less distortions. Extensive experiments in terms of visual quality and a user study are carried out on the widely used RetargetMe dataset. Experimental results show that our method outperforms current state-of-Art image retargeting methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
dc.relation.ispartof	IEEE Visual Communications and Image Processing
dc.relation.isbasedon	10.1109/VCIP47243.2019.8966008
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/openAccess
dc.title	Deep feature guided image retargeting
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Sydney, Australia
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 Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-02-09T01:16:48Z
pubs.finish-date	2019-12-04
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-12-01
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Image retargeting is the technique to display images via devices with various aspect ratios and sizes. Traditional content-Aware retargeting methods rely on low-level features to predict pixel-wise importance and can hardly preserve both the structure lines and salient regions of the source image. To address this problem, we propose a novel adaptive image warping approach which integrates with deep convolutional neural network. In the proposed method, a visual importance map and a foreground mask map are generated by a pre-Trained network. The two maps and other constraints guide the warping process to yield retargeted results with less distortions. Extensive experiments in terms of visual quality and a user study are carried out on the widely used RetargetMe dataset. Experimental results show that our method outperforms current state-of-Art image retargeting methods.

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http://hdl.handle.net/10453/145969