Haralik features : Maximal Correlation Coefficient

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Hend Abouche
Hend Abouche 2022-4-10
编辑： Serhat Yesilkoy 2022-12-14
Hi Guys, I am working on haralik features and so far I managed to find all the 13 features except for Maximal Correlation Coefficient, that should be calculated as:
I've already define the px and py:
p_x(i,k) = p_x(i,k) + glcm(i,j,k);
p_y(i,k) = p_y(i,k) + glcm(j,i,k);
p(i,j)=glcm(i,j,k)
I ahve 4 offsets
thank u in advance for ur comments
  2 个评论
Image Analyst
Image Analyst 2022-4-10
What are Q, p, px, and py? Which is your image? How did you get the glcm array? How does your equation compute Q(i, j)?
Hend Abouche
Hend Abouche 2022-4-10
编辑：Hend Abouche 2022-4-10
glcm is the results of graycomatrix(gry,'offset',offsets); using a gray image, p is co-occurence probability matrix,
px(i): i-th entry in the marginal-probability matrix obtained by summing the rows of p(i,j)
py(j): j-th entry in the marginal-probability matrix obtained by summing the columns of p(i,j)
I used this website equations https://earlglynn.github.io/RNotes/package/EBImage/Haralick-Textural-Features.html
PS: I normalized the data: glcm = glcm./sum(glcm(:));

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Image Analyst
Image Analyst 2022-4-10
You need to have 3 nested loops. The outer one would be over j (columns). The middle one would be over i (rows). The innermost one would be over k.
[rows, columns] = size(p)
for j = 1 : columns
for i = 1 : rows
numerator = 0;
denominator = 0;
for k = 1 : columns
numerator = numerator + p(i, k) * p(j, k);
denominator = denominator + px(i) * py(k);
end
Q(i, j) = sum(numerator / denominator);
end
end
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Serhat Yesilkoy
Serhat Yesilkoy 2022-12-14
编辑：Serhat Yesilkoy 2022-12-14
hi,I didn't understand the part of how the marginal probability functions px and py are defined.Unrecognized function or variable 'px'.Unrecognized function or variable 'px'. I am facing the problem.

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