A Completed Modeling of Local Binary Pattern Operator for Texture Classification

In this correspondence, a completed modeling of the local binary pattern (LBP) operator is proposed and an associated completed LBP (CLBP) scheme is developed for texture classification. A local region is represented by its center pixel and a local difference sign-magnitude transform (LDSMT). The center pixels represent the image gray level and they are converted into a binary code, namely CLBP-Center (CLBPC), by global thresholding. LDSMT decomposes the image local differences into two complementary components: the signs and the magnitudes, and two operators, namely CLBP-Sign (CLBPS) and CLBP-Magnitude (CLBPM), are proposed to code them. The traditional LBP is equivalent to the CLBPS part of CLBP, and we show that CLBPS preserves more information of the local structure than CLBPM, which explains why the simple LBP operator can extract the texture features reasonably well. By combining CLBPS, CLBPM, and CLBPC features into joint or hybrid distributions, significant improvement can be made for rotation invariant texture classification.