Affine Differential Local Mean ZigZag Pattern for Texture Classification

Abstract

The texture classification is a significant problem in the area of pattern recognition. This work proposes a novel Affine Differential Local Mean ZigZag Pattern (ADLMZP) descriptor for texture classification. The proposed method has two manifolds: first Local Mean ZigZag Pattern (LMZP) map is calculated by thresholding the 3 × 3 patch neighbor intensity values with respect to path mean but in a ZigZag weighting fashion, which provides a well discriminated descriptor compared to other local binary descriptors. The local micropattern is obtained by comparing neighbor intensity values with respect to path mean which makes the descriptor robust against noise and illumination variations. Secondly, in order to make it invariant to affine changes, we incorporated an affine differential transformation along with affine gradient magnitude information of a texture image which is differed from Euclidean Gradient. The final ADLMZP descriptor is generated by concatenating the histograms of all Affine Differential Local Mean ZigZag maps. The results are computed over well known KTH-TIPS, Brodatz, and CUReT texture datasets and compared with the state-of-the-art texture classification methods.