Enhancing LBP by preprocessing via anisotropic diffusion

Abstract

The main goal of this paper is to study the addition of a new preprocessing step in order to improve local feature descriptors and texture classification. The preprocessing is implemented by using transformations which help highlight salient features that play a significant role in texture recognition. We evaluate and compare four different competing methods: three different anisotropic diffusion methods including the classical anisotropic Perona–Malik diffusion and two subsequent regularizations of it and the application of a Gaussian kernel, which is the classical multiscale approach in texture analysis. The combination of the transformed images and the original ones are analyzed. The results show that the use of the preprocessing step does lead to an improvement in texture recognition.