Continuous rotation invariant features for gradient-based texture classification

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dc.authoridHanbay, Kazım/0000-0003-1374-1417
dc.authoridKarci, Ali/0000-0002-8489-8617
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authoridTalu, Muhammed Fatih/0000-0003-1166-8404
dc.authoridALPASLAN, Nuh/0000-0002-6828-755X
dc.authorwosidHanbay, Kazım/J-3848-2014
dc.authorwosidKARCI, Ali/A-9604-2019
dc.authorwosidKarci, Ali/AAG-5337-2019
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.authorwosidTalu, Muhammed Fatih/W-2834-2017
dc.authorwosidALPASLAN, Nuh/B-2199-2018
dc.contributor.authorHanbay, Kazim
dc.contributor.authorAlpaslan, Nuh
dc.contributor.authorTalu, Muhammed Fatih
dc.contributor.authorHanbay, Davut
dc.contributor.authorKarci, Ali
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:40:01Z
dc.date.available2024-08-04T20:40:01Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.description.abstractExtracting rotation invariant features is a valuable technique for the effective classification of rotation invariant texture. The Histograms of Oriented Gradients (HOG) algorithm has been proved to be theoretically simple, and has been applied in many areas. Also, the co-occurrence HOG (CoHOG) algorithm provides a unified description including both statistical and differential properties of a texture patch. However, HOG and CoHOG have some shortcomings: they discard some important texture information and are not invariant to rotation. In this paper, based on the original HOG and CoHOG algorithms, four novel feature extraction methods are proposed. The first method uses Gaussian derivative filters named GDF-HOG. The second and the third methods use eigenvalues of the Hessian matrix named Eig(Hess)-HOG and Eig(Hess)-CoHOG, respectively. The fourth method exploits the Gaussian and means curvatures to calculate curvatures of the image surface named GM-CoHOG. We have empirically shown that the proposed novel extended HOG and CoHOG methods provide useful information for rotation invariance. The classification results are compared with original HOG and CoHOG algorithms methods on the CUReT, KTH-TIPS, KTH-TIPS2-a and UIUC datasets show that proposed four methods achieve best classification result on all datasets. In addition, we make a comparison with several well-known descriptors. The experiments of rotation invariant analysis are carried out on the Brodatz dataset, and promising results are obtained from those experiments. (C) 2014 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/j.cviu.2014.10.004
dc.identifier.endpage101en_US
dc.identifier.issn1077-3142
dc.identifier.issn1090-235X
dc.identifier.scopus2-s2.0-84921047821en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage87en_US
dc.identifier.urihttps://doi.org/10.1016/j.cviu.2014.10.004
dc.identifier.urihttps://hdl.handle.net/11616/96658
dc.identifier.volume132en_US
dc.identifier.wosWOS:000349430600008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.ispartofComputer Vision and Image Understandingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHOGen_US
dc.subjectCoHOGen_US
dc.subjectHessian matrixen_US
dc.subjectEigen analysisen_US
dc.subjectRotation invarianceen_US
dc.subjectTexture classificationen_US
dc.titleContinuous rotation invariant features for gradient-based texture classificationen_US
dc.typeArticleen_US

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