Objects co-segmentation: Propagated from simpler images

Chen, M; Velasco-Forero, S; Tsang, I; Cham, TJ

Objects co-segmentation: Propagated from simpler images

Chen, M Velasco-Forero, S Tsang, I

Cham, TJ

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Publication Type:: Conference Proceeding
Citation:: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015, 2015-August pp. 1682 - 1686
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Chen, M	en_US
dc.contributor.author	Velasco-Forero, S	en_US
dc.contributor.author	Tsang, I https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Cham, TJ	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2015, 2015-August pp. 1682 - 1686	en_US
dc.identifier.isbn	9781467369978	en_US
dc.identifier.issn	1520-6149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121757
dc.description.abstract	© 2015 IEEE. Recent works on image co-segmentation aim to segment common objects among image sets. These methods can co-segment simple images well, but their performance may degrade significantly on more cluttered images. In order to co-segment both simple and complex images well, this paper proposes a novel paradigm to rank images and to propagate the segmentation results from the simple images to more and more complex ones. In the experiments, the proposed paradigm demonstrates its effectiveness in segmenting large image sets with a wide variety in object appearance, sizes, orientations, poses, and multiple objects in one image. It outperformed the current state-of-the-art algorithms significantly, especially in difficult images.	en_US
dc.relation.ispartof	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en_US
dc.relation.isbasedon	10.1109/ICASSP.2015.7178257	en_US
dc.title	Objects co-segmentation: Propagated from simpler images	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-August	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2015-August	en_US

Abstract:

© 2015 IEEE. Recent works on image co-segmentation aim to segment common objects among image sets. These methods can co-segment simple images well, but their performance may degrade significantly on more cluttered images. In order to co-segment both simple and complex images well, this paper proposes a novel paradigm to rank images and to propagate the segmentation results from the simple images to more and more complex ones. In the experiments, the proposed paradigm demonstrates its effectiveness in segmenting large image sets with a wide variety in object appearance, sizes, orientations, poses, and multiple objects in one image. It outperformed the current state-of-the-art algorithms significantly, especially in difficult images.

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http://hdl.handle.net/10453/121757