Feature matching using Euclidean

I am now doing copy move image forgery detection using DCT. Now I have some problem for matching part. It lagging for loading whole data since I am using looping to get the similarity between two row vector. Any idea to change my code in better way. Here is my code for the matching part.

z=0;
for num=1:group    
    for a=mS(num):mE(num)-1
        for b=a+1:mE(num)
            z=z+1;
            Dsimilar(z,1) = norm(sort(a,:)-sort(b,:))
        end
    end
end

Answer 1

[Walter Roberson]

That is not a valid calling form for the sort() function, so that code will not execute.

Why would you want to sort a single integer anyhow?

[Im]

Sorry for wrong typing, that sort is a naming for my matrix.

z=0;
  for num=1:group    
      for a=mS(num):mE(num)-1
          for b=a+1:mE(num)
              z=z+1;
              Dsimilar(z,1) = norm(sortZZ(a,:)-sortZZ(b,:))
          end
      end
  end

[Walter Roberson]

norm() by default calculates the 2-norm, which is the Euclidean length of the vector. Which is sqrt(sum(x.^2)) . You can calculate a lot of euclidean lengths at once. For example,

for a = Ms(num):Me(num)+1
  Ta = repmat(sortZZ(a,:), Me(num)-a, 1);
  Tb = sortZZ(a+1:me(num),:);
  norms = sqrt(sum((Ta - Tb).^2, 2));
  Dsimilar = [Dsimilar; norms(:)];
end

[Im]

Thanks a lot. I get it.