Statistical radial binary patterns (SRBP) for bark texture identification

Abstract

This paper presents a plant identification method based on the texture characterization of bark images. We propose a novel statistical radial binary pattern (SRBP) descriptor to encode the between-scale texture information within large neighbourhood areas using the statistical description of the grey scale intensity distribution. The proposed descriptor can efficiently encode the macro local structure. In addition, the proposed SRBP is computationally simple, rotation invariant and low-dimensional descriptor. We conduct comprehensive experiments on three different bark datasets to assess the performances of our approach. The experimental results show that our method achieves high identification rates outperforming different multi-scale LBP variants.