Principal curvatures based rotation invariant algorithms for efficient texture classification

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dc.authoridHanbay, Kazım/0000-0003-1374-1417
dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridALPASLAN, Nuh/0000-0002-6828-755X
dc.authorwosidHanbay, Kazım/J-3848-2014
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidALPASLAN, Nuh/B-2199-2018
dc.contributor.authorHanbay, Kazim
dc.contributor.authorAlpaslan, Nuh
dc.contributor.authorTalu, Muhammed Fatih
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:42:31Z
dc.date.available2024-08-04T20:42:31Z
dc.date.issued2016
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe histograms of oriented gradients (HOG) and co-occurrence HOG (CoHOG) algorithms are simple and intuitive descriptors. However, the HOG and CoHOG algorithms based on gradient computation still have some shortcomings: they ignore meaningful textural properties and are unstable to noise. In this paper, two new efficient HOG and CoHOG methods are proposed. The proposed algorithms are based on the Gaussian derivative filters, and the feature vectors are obtained by means of principal curvatures. The feature vectors are rotation invariant by means of the rotation invariance characteristic of principal curvatures (i.e. eigenvalues). The experimental results on the CUReT, ICTH-TIPS, KTH-11PS2-a, UIUC, Brodatz album, Kylberg and Xu datasets confirm that the developed algorithms have higher classification rates than state-of-the-art texture classification methods. The classification results also demonstrate that the developed algorithms are more stable to noise and rotation than the original HOG and CoHOG algorithms. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.neucom.2016.03.032
dc.identifier.endpage89en_US
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.scopus2-s2.0-84978319136en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage77en_US
dc.identifier.urihttps://doi.org/10.1016/j.neucom.2016.03.032
dc.identifier.urihttps://hdl.handle.net/11616/97412
dc.identifier.volume199en_US
dc.identifier.wosWOS:000377230400008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofNeurocomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFeature extractionen_US
dc.subjectPrincipal curvaturesen_US
dc.subjectRotation invarianceen_US
dc.subjectTexture classificationen_US
dc.titlePrincipal curvatures based rotation invariant algorithms for efficient texture classificationen_US
dc.typeArticleen_US

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