Local Binary Pattern Regrouping for Rotation Invariant Texture Classification

摘要

This paper represents 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 domains. The analysis of LBP's 256 patterns has led the authors to propose a new organization of uniform and no uniform patterns into 28 groups; each group assembled a number of patterns that 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.