Visual attention based small object segmentation in natual images

Guo, W; Xu, C; Ma, S; Xu, M

Visual attention based small object segmentation in natual images

Guo, W Xu, C Ma, S Xu, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Image Processing, ICIP, 2010, pp. 1565 - 1568
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Guo, W	en_US
dc.contributor.author	Xu, C	en_US
dc.contributor.author	Ma, S	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Image Processing, ICIP, 2010, pp. 1565 - 1568	en_US
dc.identifier.isbn	9781424479948	en_US
dc.identifier.issn	1522-4880	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16300
dc.description.abstract	Small object segmentation is a challenging task in image processing and computer vision. In this paper we propose a visual attention based segmentation approach to segment interesting objects with small size in natural images. Different from traditional methods which use the single feature vectors, visual attention analysis is used on local and global features to extract the region of interesting objects. Within the region selected by visual attention analysis, Gaussian Mixture Model (GMM) is applied to further locate the object region. By incorporation of visual attention analysis into object segmentation, the proposed approach is able to narrow the searching region for object segmentation so as to increase the segmentation accuracy and reduce the computational complex. Experimental results demonstrate that the proposed approach is efficient for object segmentation in natural images, especially for small objects. The proposed method outperforms traditional GMM based segmentation significantly. © 2010 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Image Processing, ICIP	en_US
dc.relation.isbasedon	10.1109/ICIP.2010.5649841	en_US
dc.title	Visual attention based small object segmentation in natual images	en_US
dc.type	Conference Proceeding
utslib.for	080106 Image Processing	en_US
dc.location.activity	Hongkong	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.publication-status	Published	en_US

Abstract:

Small object segmentation is a challenging task in image processing and computer vision. In this paper we propose a visual attention based segmentation approach to segment interesting objects with small size in natural images. Different from traditional methods which use the single feature vectors, visual attention analysis is used on local and global features to extract the region of interesting objects. Within the region selected by visual attention analysis, Gaussian Mixture Model (GMM) is applied to further locate the object region. By incorporation of visual attention analysis into object segmentation, the proposed approach is able to narrow the searching region for object segmentation so as to increase the segmentation accuracy and reduce the computational complex. Experimental results demonstrate that the proposed approach is efficient for object segmentation in natural images, especially for small objects. The proposed method outperforms traditional GMM based segmentation significantly. © 2010 IEEE.

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