Information such as contrast, entropy, dissimilarity index and variance are computed to know the properties of any image, but how these values are used to for further classification or feature extraction?
Image Texture Classification using Gray Level Co-Occurrence Matrix Based Statistical Features
In this paper, a novel texture classification system based on Gray Level Co-occurrence Matrix (GLCM) is presented. The texture classification is achieved by extracting the spatial relationship of pixel in the GLCM. In the proposed method, GLCM is calculated from the original texture image and the differences calculated along the first non singleton dimension of the input texture image.
Article Image Texture Classification using Gray Level Co-Occurrence ...
Dear researcher
You can use the following process for feature extraction based on GLCM:
1- Compute GLCM for input image (different size,angles,etc)
2- Computer value of entropy, energy, skew, mean, etc from provided Gray-level co-occurrence matrix
3- Consider a numeric feature vector with N dimensions. Each dimension shows the value of one of the computed measures
For example final extracted feature vector is :
(For example)
4 - Finally you can classify textures based on these feature vectors and using a suitable classifier such as KNN, KD, SVM, DT, etc.
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