An Improved Local Ternary Pattern for Texture Classification

Abstract

In this study, we proposed a new operator known as the synchronized rotation local ternary pattern (SRLTP) for texture classification. The proposed SRLTP descriptor improves on the local ternary pattern (LTP) method with an additional process on the generated lower and upper LTPs. The lower and upper patterns are encoded to a rotation invariant pattern histogram and a uniform pattern histogram, respectively. Thus, the feature vector can utilize the advantages offered by the rotation invariant pattern histogram while retaining the original information in the uniform pattern histogram. Moreover, in this study, a two-dimensional discrete wavelet transform (DWT) and a discrete Fourier transform (DFT) enhanced the robustness of the texture classification. The experimental results demonstrate that the performance of the SRLTP descriptor is better than those of the existing descriptors.