A Novel Extended Mapping of Local Binary Pattern for Texture Classification

Texture classification is one of the important branches of image processing. The main point of texture classification is feature extraction. Local Binary Pattern (LBP) is one of the important methods that are used for texture feature extraction. This method is widely used because it has simple implementation and extracts high discriminative features from textures. Most of previous LBP methods used uniform patterns and only one feature is extracted from non-uniform patterns. In this paper, by extending non-uniform patterns a new mapping technique is proposed that extracts more discriminative features from non-uniform patterns. So in spite of almost all of the previous LBP methods, the proposed method extracts more discriminative features from non-uniform patterns and increases the classification accuracy of textures.
The proposed method has all of the positive points of previous LBP variants. It is a rotation invariant and illumination invariant method and increase the classification accuracy. The implementation of proposed mapping on Outex dataset shows that proposed method can improve the accuracy of classifications significantly.