Robustness of rotation invariant descriptors for texture classification

Abstract

In this paper, we present an evaluation of texture descriptors’ robustness when interpolation methods are applied over rotated images. We propose a novel rotation invariant texture descriptor called Sampled Local Mapped Pattern Magnitude (SLMP M) and we compare it with well-known published texture descriptors. The compared descriptors are the Completed Local Binary Pattern (CLBP), and two Discrete Fourier Transform (DFT)-based methods called the Local Ternary Pattern DFT and the Improved Local Ternary Pattern DFT. Experiments were performed on the Kylberg Sintorn Rotation Dataset, a database of natural textures that were rotated using hardware and computational procedures. Five interpolation methods were investigated: Lanczos, B-spline, Cubic, Linear and Nearest Neighbor with nine directions. Experimental results show that our proposed method makes a robust texture discrimination, overcoming traditional texture descriptors and works better in different interpolations.