Improved 3D Co-Occurrence Matrix for Texture Description and Classification

This paper proposes an improved feature extraction method for volumetric texture classification. Our approach consists in the computation of 3D co-occurrence matrices built using both the image intensity and the gradient image information. The feature vector represents the concatenation of the Haralick second-order statistics and the proposed gradient-based and orientation-based indicators. The results obtained on a public synthetic 3D texture database show that the proposed technique is more discriminative and brings improvements in the classification performance when compared to recent 3D and 2D texture descriptors.