Rotation-invariant features based on directional coding for texture classification

Abstract

A directional coding (DC) method is proposed to extract rotation-invariant features for texture classification. DC uses four orientations in $$3\times 3$$ neighborhood pixel. For each orientation, the rank order of the central gray-level pixel is calculated. The four ranks are used to get 15 codes. The codes are combined with the information of the central pixel to extract 30 rotation-invariant features. For a multi-resolution study, DC is calculated by altering the window size around a central pixel. The number of samples is restricted to eight neighbors by local averaging. Therefore, in each single-scale DC histogram, the number of bins is kept small and constant. Outex, CUReT and KTH_TIPS2 databases are used to evaluate and compare the proposed method against some state-of-the-art local binary techniques and other texture analysis methods. The results obtained suggest that the proposed DC method outperforms other methods making it attractive for use in computer vision problems.