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Local Binary Pattern Regrouping for Rotation Invariant Texture Classification

Local Binary Pattern Regrouping for Rotation Invariant Texture Classification

Zitouni Asma, Nini Brahim
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 15
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.299945
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MLA

Asma, Zitouni, and Nini Brahim. "Local Binary Pattern Regrouping for Rotation Invariant Texture Classification." JITR vol.15, no.1 2022: pp.1-15. http://doi.org/10.4018/JITR.299945

APA

Asma, Z. & Brahim, N. (2022). Local Binary Pattern Regrouping for Rotation Invariant Texture Classification. Journal of Information Technology Research (JITR), 15(1), 1-15. http://doi.org/10.4018/JITR.299945

Chicago

Asma, Zitouni, and Nini Brahim. "Local Binary Pattern Regrouping for Rotation Invariant Texture Classification," Journal of Information Technology Research (JITR) 15, no.1: 1-15. http://doi.org/10.4018/JITR.299945

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Abstract

This paper represent a deep study of the Local Binary Pattern (LBP) method and its variants of patterns regrouping , which is largely used in texture classification as well in other domain. The analysis of LBP’s two hundred fifty-six patterns has led us to propose a new organization of uniform and no uniform patterns into twenty-eight groups; each group assembled a number of patterns varied according to specific terms. The principal idea is to preserve the low complexity of LBP and simultaneously increase the method robustness against quality degradation caused by image operations like rotation, grey level changes, illumination and mirror effects. The experiments are done with the two texture databases Outex and Brodatz; the tests are proving the robustness of Local Binary Pattern Regrouping (LBPG) under circumstances.