MEGH: A new affine invariant descriptor

Dong, X; Liu, E; Yang, J; Wu, Q

MEGH: A new affine invariant descriptor

Dong, X Liu, E Yang, J Wu, Q

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Publication Type:: Journal Article
Citation:: KSII Transactions on Internet and Information Systems, 2013, 7 (7), pp. 1690 - 1704
Issue Date:: 2013-07-30

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Field	Value	Language
dc.contributor.author	Dong, X	en_US
dc.contributor.author	Liu, E	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.date.issued	2013-07-30	en_US
dc.identifier.citation	KSII Transactions on Internet and Information Systems, 2013, 7 (7), pp. 1690 - 1704	en_US
dc.identifier.issn	1976-7277	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27880
dc.description.abstract	An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments. © 2013 KSII.	en_US
dc.relation.ispartof	KSII Transactions on Internet and Information Systems	en_US
dc.relation.isbasedon	10.3837/tiis.2013.07.010	en_US
dc.title	MEGH: A new affine invariant descriptor	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	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.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments. © 2013 KSII.

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http://hdl.handle.net/10453/27880