Symmetric color ratio in spiral architecture

Jia, W; Zhang, H; He, X; Wu, Q

Symmetric color ratio in spiral architecture

Jia, W

Zhang, H He, X

Wu, Q

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 3852 LNCS pp. 204 - 213
Issue Date:: 2006-06-14

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Field	Value	Language
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.date.issued	2006-06-14	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 3852 LNCS pp. 204 - 213	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4952
dc.description.abstract	Color ratio gradient (CRG) is a robust method used for color image retrieval and object recognition. It has been proven to be illumination-independent and geometry-insensitive when tested on scenery images. However, the color ratio gradient produces unsatisfying matching results when dealing with an object which appears rotated by a certain relative angle in the model and target images. In this paper, we adopt the idea of color ratio gradient and develop a new method called Symmetric Color Ratio (SCR) based on a hexagonal image structure, the Spiral Architecture (SA). We focus on license plate images and our aim is to achieve a higher matching rate between the SCR histogram of the images within same class in order to separate different classes of images. Our experimental results demonstrate that the proposed SCR is robust to changes over view angles. © Springer-Verlag Berlin Heidelberg 2006.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0451666
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/11612704_21	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Symmetric color ratio in spiral architecture	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3852 LNCS	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Hyderabad, India	en_US
dc.location.activity	Atlanta, GA
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	open_access
pubs.publication-status	Published	en_US
pubs.volume	3852 LNCS	en_US

Abstract:

Color ratio gradient (CRG) is a robust method used for color image retrieval and object recognition. It has been proven to be illumination-independent and geometry-insensitive when tested on scenery images. However, the color ratio gradient produces unsatisfying matching results when dealing with an object which appears rotated by a certain relative angle in the model and target images. In this paper, we adopt the idea of color ratio gradient and develop a new method called Symmetric Color Ratio (SCR) based on a hexagonal image structure, the Spiral Architecture (SA). We focus on license plate images and our aim is to achieve a higher matching rate between the SCR histogram of the images within same class in order to separate different classes of images. Our experimental results demonstrate that the proposed SCR is robust to changes over view angles. © Springer-Verlag Berlin Heidelberg 2006.

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http://hdl.handle.net/10453/4952