3D Texture Feature Extraction and Classification using the BM3DELBP approach

Abstract

This paper presents a new feature extraction technique for 3D texture classification based on Local Binary Patterns-derived operators and state-of-the-art denoising techniques, with good invariance properties to different transformations and robust to noise. Our approach consists in the computation of textural features obtained using the Block Matching and 3D Filtering Extended Local Binary Patterns for each of the three orthogonal planes and their concatenation. The proposed method achieves a promising classification performance on a publicly available dataset of 3D textured images. The obtained results prove that the presented feature extraction technique is above traditional 2D texture descriptors and typical CNN deep learning-based methods.