A comparison on histogram based image matching methods

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

A comparison on histogram based image matching methods

Jia, W

Zhang, H He, X

Wu, Q

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, 2006
Issue Date:: 2006-12-01

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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	2006-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, 2006	en_US
dc.identifier.isbn	0769526888	en_US
dc.identifier.isbn	9780769526881	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2958
dc.description.abstract	Using colour histogram as a stable representation over change in view has been widely used for object recognition. In this paper, three newly proposed histogram-based methods are compared with other three popular methods, including conventional histogram intersection (HI) method, Wong and Cheung's merged palette histogram matching (MPHM) method, and Gevers' colour ratio gradient (CRG) method. These methods are tested on vehicle number plate images for number plate classification. Experimental results disclose that, the CRG method is the best choice in terms of speed, and the GWHI method can give the best classification results. Overall, the CECH method produces the best performance when both speed and classification performance are concerned. © 2006 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0451666
dc.relation	http://purl.org/au-research/grants/arc/DP0452657
dc.relation.ispartof	Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006	en_US
dc.relation.isbasedon	10.1109/AVSS.2006.5	en_US
dc.title	A comparison on histogram based image matching methods	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	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 - 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

Abstract:

Using colour histogram as a stable representation over change in view has been widely used for object recognition. In this paper, three newly proposed histogram-based methods are compared with other three popular methods, including conventional histogram intersection (HI) method, Wong and Cheung's merged palette histogram matching (MPHM) method, and Gevers' colour ratio gradient (CRG) method. These methods are tested on vehicle number plate images for number plate classification. Experimental results disclose that, the CRG method is the best choice in terms of speed, and the GWHI method can give the best classification results. Overall, the CECH method produces the best performance when both speed and classification performance are concerned. © 2006 IEEE.

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http://hdl.handle.net/10453/2958