Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform

• We present a method to improve accuracy and robustness of co-occurrence features in rotation invariant image classification.
• Co-occurrences are computed through digital circles.
• Rotation invariance is obtained through discrete Fourier transform normalisation.
• We tested or method on four different datasets.
• Experiments show that our approach is more accurate and robust against rotation than the traditional one.

Grey-level co-occurrence matrices (GLCM) have been on the scene for almost forty years and continue to be widely used today. In this paper we present a method to improve accuracy and robustness against rotation of GLCM features for image classification. In our approach co-occurrences are computed through digital circles as an alternative to the standard four directions. We use discrete Fourier transform normalisation to convert rotation dependent features into rotation invariant ones. We tested our method on four different datasets of natural and synthetic images. Experimental results show that our approach is more accurate and robust against rotation than the standard GLCM features.