One-dimensional Grey-level Co-occurrence Matrices for texture classification

The grey-level co-occurrence matrices (GLCM) has been widely used for various texture analysis implementations and has provided satisfying results. The conventional GLCM method is two dimensional as it focus on the co-occurrence of the specific pixel pairs. The one-dimensional GLCM reduces the matri...

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Main Authors: Jing Yi Tou, Yong Haur Tay, Phooi Yee Lau
Format: Conference Proceeding
Language:English
Subjects:
Distance measurement
Entropy
Feature extraction
Neurons
Pixel
Testing
Training
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Yong Haur Tay
Phooi Yee Lau
description The grey-level co-occurrence matrices (GLCM) has been widely used for various texture analysis implementations and has provided satisfying results. The conventional GLCM method is two dimensional as it focus on the co-occurrence of the specific pixel pairs. The one-dimensional GLCM reduces the matrices to a single dimension by focusing only on the differences of the grey level between pixel pairs. The experiment results on 32 Brodatz textures shows that in a same setting, the one-dimensional GLCM achieved a recognition rate of 83.01% while the conventional GLCM achieved a recognition rate of 81.35%. The results show that the one-dimensional GLCM can perform as good as the conventional GLCM but with fewer computations involved.
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subjects Distance measurement
Entropy
Feature extraction
Neurons
Pixel
Testing
Training
title One-dimensional Grey-level Co-occurrence Matrices for texture classification
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