Random Sampling Local Binary Pattern Encoding Based on Gaussian Distribution

Abstract

The original local binary pattern (LBP) operator or LBP variants adopt the difference between the neighboring pixels and the center pixel to describe the pixel that does not consider the relationship between the neighboring pixels. The block region characteristics of an image are determined by the relationship between neighboring pixels, not just the neighboring pixels and the center pixel. In this letter, a new local neighborhood encoding method is proposed, which we call random sampling LBP (RSLBP). Based on the distribution of the image difference signal, point pairs are randomly selected in the local neighborhood, and LBP encoding is carried out after comparing the sums of pixels neighboring the random point. Image local difference is more obvious and noise resistance is better. By comparing the classification results of LBP and LBP variants with the proposed method, we show that the proposed method achieves better classification performance on the standard images library and the real fastener images, and the performance gain is significant when the noise level is high.