Fractional-order gradient based local binary pattern for texture classification

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dc.contributor.authorAlpaslan, Nuh
dc.contributor.authorHanbay, Kazim
dc.date.accessioned2026-04-04T13:35:10Z
dc.date.available2026-04-04T13:35:10Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThe local binary patterns method plays an efficient role in texture classification and feature extraction. These approaches extract textural features by using the neighboring pixel values. The single or joint histogram of the texture image is constructed from the LBP features obtained from local relationships. In this study, a method of utilizing fractional derivative information effectively has been proposed for classifying color texture images. The magnitude of the fractional horizontal and vertical derivatives obtained with Gaussian derivative filters are integrated into the ACS-LBP method. The magnitude information of the fractional derivatives of local texture patterns has been modeled according to the relationship between neighboring pixels. The computed derivative information has been incorporated into the ACS-LBP model to effectively encode the local pixel relationship. In order to maintain, these fractional-order edge and texture transition detection operators provide both high robustness and continue to detect small textural details. To accomplish these capabilities, the fractional-order parameter is tuned to target particular pixel transition frequencies. This gives the proposed LBP method greater latitude in selecting the fractional-order mask. An additional degree of freedom in designing various masks is provided by the fractional-order parameter. The developed model has been evaluated on widely used texture databases. It also has been compared with existing LBP and deep learning models in terms of different performance metrics. The proposed method has shown significant advantages over up to date methods in both classification accuracy and execution time.
dc.identifier.doi10.1016/j.compeleceng.2025.110316
dc.identifier.issn0045-7906
dc.identifier.issn1879-0755
dc.identifier.orcid0000-0002-6828-755X
dc.identifier.scopus2-s2.0-105002329264
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2025.110316
dc.identifier.urihttps://hdl.handle.net/11616/109667
dc.identifier.volume124
dc.identifier.wosWOS:001470915800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofComputers & Electrical Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectLocal binary patterns
dc.subjectFractional derivative
dc.subjectTexture classification
dc.titleFractional-order gradient based local binary pattern for texture classification
dc.typeArticle

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