A multiple features distance preserving (MFDP) model for saliency detection

Guo, D; Zhang, J; Xu, M; He, X; Li, M; Zhao, C

A multiple features distance preserving (MFDP) model for saliency detection

Guo, D Zhang, J

Xu, M

He, X

Li, M Zhao, C

Permalink

Publication Type:: Conference Proceeding
Citation:: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 2015
Issue Date:: 2015-01-12

Closed Access

	Filename	Description	Size
	07008087.pdf	Published version	528.76 kB	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	Guo, D	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Li, M	en_US
dc.contributor.author	Zhao, C	en_US
dc.date.issued	2015-01-12	en_US
dc.identifier.citation	2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, 2015	en_US
dc.identifier.isbn	9781479954094	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34119
dc.description.abstract	© 2014 IEEE. Playing a vital role, saliency has been widely applied for various image analysis tasks, such as content-aware image retargeting, image retrieval and object detection. It is generally accepted that saliency detection can benefit from the integration of multiple visual features. However, most of the existing literatures fuse multiple features at saliency map level without considering cross-feature information, i.e. generate a saliency map based on several maps computed from an individual feature. In this paper, we propose a Multiple Feature Distance Preserving (MFDP) model to seamlessly integrate multiple visual features through an alternative optimization process. Our method outperforms the state-of-the-arts methods on saliency detection. Saliency detected by our method is further cooperated with seam carving algorithm and significantly improves the performance on image retargeting.	en_US
dc.relation.ispartof	2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014	en_US
dc.relation.isbasedon	10.1109/DICTA.2014.7008087	en_US
dc.title	A multiple features distance preserving (MFDP) model for saliency detection	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Wollongong
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 - CRIN - Realtime Information Networks
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.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Playing a vital role, saliency has been widely applied for various image analysis tasks, such as content-aware image retargeting, image retrieval and object detection. It is generally accepted that saliency detection can benefit from the integration of multiple visual features. However, most of the existing literatures fuse multiple features at saliency map level without considering cross-feature information, i.e. generate a saliency map based on several maps computed from an individual feature. In this paper, we propose a Multiple Feature Distance Preserving (MFDP) model to seamlessly integrate multiple visual features through an alternative optimization process. Our method outperforms the state-of-the-arts methods on saliency detection. Saliency detected by our method is further cooperated with seam carving algorithm and significantly improves the performance on image retargeting.

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

http://hdl.handle.net/10453/34119