Completed Grayscale-Inversion and Rotation Invariant Local Binary Pattern for Texture Classification

Abstract

Local binary pattern (LBP) and its variants (e.g., LTP and CLBP) are powerful descriptors for texture analysis. However, most of these LBP-based methods are sensitive to inverse grayscale changes. To overcome this problem, we present a novel texture descriptor named Completed Grayscale-Inversion and Rotation Invariant Local Binary Pattern (CGRI-LBP). CGRI-LBP is based on the framework of CLBP which jointly encodes three components (i.e., the signs and magnitudes of local differences as well as central pixels) but with two significant improvements: 1) the sign information of local differences is encoded by a rotation-invariant complementary coding scheme, and 2) the intensity information of central pixels is encoded via a dominant intensity order measure. Extensive experiments on three texture databases (Outex, CUReT and KTH-TIPS) demonstrate that the proposed descriptor achieves the state-of-the-art classification performance in the presence of linear and even nonlinear grayscale-inversion changes.