Visual Information Fusion For Object-based Video Image Segmentation Using Unsupervised Bayesian Online Learning

Jia, Z; Balasuriya, A; Challa, S

Visual Information Fusion For Object-based Video Image Segmentation Using Unsupervised Bayesian Online Learning

Jia, Z Balasuriya, A Challa, S

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Publisher:: Inst Engineering Technology-iet
Publication Type:: Journal Article
Citation:: Iet Image Processing, 2007, 1 (2), pp. 168 - 181
Issue Date:: 2007-01

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Field	Value	Language
dc.contributor.author	Jia, Z	en_US
dc.contributor.author	Balasuriya, A	en_US
dc.contributor.author	Challa, S	en_US
dc.date.issued	2007-01	en_US
dc.identifier.citation	Iet Image Processing, 2007, 1 (2), pp. 168 - 181	en_US
dc.identifier.issn	1751-9659	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4182
dc.description.abstract	An algorithm using the unsupervised Bayesian online learning process is proposed for the segmentation of object-based video images. The video image segmentation is solved using a classification method. First, different visual features (the spatial locati	en_US
dc.publisher	Inst Engineering Technology-iet	en_US
dc.relation.ispartof	Iet Image Processing	en_US
dc.relation.isbasedon	10.1049/iet-ipr:20050346	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Visual Information Fusion For Object-based Video Image Segmentation Using Unsupervised Bayesian Online Learning	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	1	en_US
utslib.location.activity	ISI:000249578200008	en_US
utslib.for	0999 Other Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	ISI:000249578200008	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 Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	2	en_US
pubs.volume	1	en_US

Abstract:

An algorithm using the unsupervised Bayesian online learning process is proposed for the segmentation of object-based video images. The video image segmentation is solved using a classification method. First, different visual features (the spatial locati

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

http://hdl.handle.net/10453/4182