Bibliographic details

Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform, Pattern Recognition Letters, 48:34-41,2014. Special issue 'Celebrating the life and work of Maria Petrou'.

About the authors

Francesco Bianconi is Lecturer in the Department of Industrial Engineering at the University of Perugia, Italy.
E-mail: bianco@ieee.org

Antonio Fernández* is Senior Lecturer in the School of Industrial Engineering at the University of Vigo, Spain.
E-mail: antfdez@uvigo.es


*Corresponding author

Abstract

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 normalization 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.




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