Dipartimento d'Ingegneria

Tuesday, 02 September 2014 09:55

Improving co-occurrence features through digital circles and DFT

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Grey-level co-occurrence matrices (GLCM) have been on the scene for almost forty years and continue to be widely used today. We developed a method to improve accuracy and robustness against rotation of GLCM features for image classification. 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 or 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.

PET 1 PET 2PET 3


Source:
F Bianconi and A. Fern√°ndez, 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'.
Code and data available here.
Read 22928 times Last modified on Tuesday, 02 September 2014 10:20

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