It might be worthwhile to read the original paper by Haralick et al, which includes feature definitions. Entropy, correlation and energy features are calculated on the resultant GLCM matrix.

Article Texture for image classification

You have generated graycomatrix of particular image using MATLAB, and it would be unique for different images. Now just use graycoprops of the GLCM. Which is GLCM property of the image of which you have generated graycomatrix.

in_img = imread('cameraman.tif');

glcm1 = graycomatrix(in_img);

glcm_prop = graycoprops(glcm1);

Here, glcm_prop will return all the 4 feature of the image.

Dear Researcher

you can do like this below code.this is jst an example

GLCM = [0 1 2 3;1 1 2 3;1 0 2 0;0 0 0 3];

stats = graycoprops(GLCM)

I = imread('circuit.tif');

GLCM2 = graycomatrix(I,'Offset',[2 0;0 2]);

stats = graycoprops(GLCM2,{'contrast','homogeneity'})

Regards

you can check this also.

http://support.echoview.com/WebHelp/Reference/Algorithms/Operators/GLCM_textures/About_the_GLCM_and_textures.htm

thanks to all, for your response.

thanx

Entropy: A concept that is not a physical quantity

https://www.researchgate.net/publication/230554936_Entropy_A_concept_that_is_not_a_physical_quantity

1. First, read the image.

2. Calculate co-occurrence matrix using graycomatrix () Matlab command

3. Finally, extract texture features (contrast, energy, homogeneity and correlation) using graycoprops () Matlab command from the calculated co-occurrence matrix