How is "Joint class histogram" algorithm deploy using Matlab? Image registration and image retrieval depend on image similarity measure to find mutual information between 2 images. Using histogram, joint histogram, and joint class histogram we can find the degree of similarity between those images. Hopefully, I understand and deploy both first techniques.Here, I found joint histogram algorithm and deploy using Matlab. 

For example:
this paper was mention this histogram and used as image similarity measurement, but I couldn't found how the authors calculate it.

However, I search for many days to find good paper, book or article regarding joint class histogram but their authors only mention this technique without enough detail. I think this technique still infant and doesn't have enough study yet.

I think joint class histogram algorithm will the best answer for my question.
I get the answer from another source and I deploy like this

% load floating and fix image

%% image segmentation using k-mean clustring.
idx_fix = kmeans(double(fix(:)),4);    
idx_float = kmeans(double(float(:)),4);

%% display image segmentation
mat1 = reshape(idx_fix,[],500);%besed on your image size
mat2  = reshape(idx_float,[],500);
imshow(mat1/max(mat1(:))); title('Fix image class');
figure;
imshow(mat2/max(mat2(:))); title('Float image class');

%% Joint class histogram
joint = zeros(5,5);%besed on number of class    
for i=1:length(idx_fix)
   c1 = idx_fix(i) + 1;%offset matrix by 1 because index start by 1
   c2 = idx_float(i) + 1;
   joint(c1, c2) =  joint(c1,c2) + 1;    
end


If the image aligned correctly the joint class histogram result as a diagonal line.



Thanks, Walter Roberson