Two novel local binary pattern descriptors for texture analysis, Applied Soft Computing, 34(2015), 728-735

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dc.contributor.authorKaya, Yılmaz
dc.contributor.authorErtuğrul, Ömer
dc.contributor.authorTekin, Ramazan
dc.date.accessioned2017-05-08T17:16:44Z
dc.date.available2017-05-08T17:16:44Z
dc.date.issued2015
dc.departmentBelirleneceken_US
dc.description.abstractThe recent developments in the image quality, storage and data transmission capabilities increase the importance of texture analysis, which plays an important role in computer vision and image processing. Local binary pattern (LBP) is an effective statistical texture descriptor, which has successful applications in texture classification. In this paper, two novel descriptors were proposed to search different patterns in images built on LBP. One of them is based on the relations between the sequential neighbors with a specified distance and the other one is based on determining the neighbors in the same orientation through central pixel parameter. These descriptors are tested with the Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets to show the applicability of the proposed nLBPd and dLBP? descriptors. The proposed methods are also compared with classical LBP. The average accuracies obtained by ANN with 10 fold cross validation, which are 99.26% (LBPu2 and nLBPd), 94.44% (dLBP?), 95.71% (View the MathML source) and %99.64 (nLBPd), for Brodatz-1, Brodatz-2, Butterfly and Kylberg datasets, respectively, show that the proposed methods outperform significant accuracies.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12604/545
dc.language.isoenen_US
dc.relation.publicationcategoryUluslararası Hakemli Dergi Makalesien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz#KayıtKontrol#
dc.subjectLocal binary patterns; nLBPd; dLBPα; Texture classification; Feature extraction; Image classificationen_US
dc.titleTwo novel local binary pattern descriptors for texture analysis, Applied Soft Computing, 34(2015), 728-735en_US
dc.typeArticleen_US

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