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Vectorize for loop: corr2(A(:,:,i),B(:,:,i))

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William Thielicke
William Thielicke 2020 年 12 月 3 日
コメント済み: Bruno Luong 2020 年 12 月 3 日
Hi, I am trying to accelerate a function and am unable to perform this myself, so I am hoping for your help.
I have a set of 10.000 small images (64x64), and I need to calculate the correlation coefficient for each of these images. This is the code:
clear all
clc
close all
A=rand(64,64,10000);
B=rand(64,64,10000);
corr_result=zeros(1,1,size(A,3));
tic
for i=1:size(A,3)
corr_result(i)=corr2(A(:,:,i),B(:,:,i));
end
toc
I found this, it results in a 64x64x1 matrix, but I need a 1x1x10000 matrix.... Thanks for your input!!
  5 件のコメント
Ameer Hamza
Ameer Hamza 2020 年 12 月 3 日
I think this is already as efficient as it can get in MATLAB. After JIT optimizations, for-loops are not as slow as one might think.
William Thielicke
William Thielicke 2020 年 12 月 3 日
But Matlab is only using 50% of my CPU during this operation. I bet there is a faster way...

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Bruno Luong
Bruno Luong 2020 年 12 月 3 日
編集済み: Bruno Luong 2020 年 12 月 3 日
If you have R2020b, you mght try to vectorize with pagemtimes function (or use mtimesx from File exchange)
meanA = mean(A,[1 2]);
meanB = mean(B,[1 2]);
Ac = A-meanA;
Bc = B-meanB;
Ac = reshape(Ac,[],1,size(A,3));
Bc = reshape(Bc,[],1,size(B,3));
% psfun = @(a,b) sum(a.*b,1);
psfun = @(a,b) pagemtimes(a,'transpose',b,'none');
C = psfun(Ac,Bc)./sqrt(psfun(Ac,Ac).*psfun(Bc,Bc))
  3 件のコメント
William Thielicke
William Thielicke 2020 年 12 月 3 日
... but beware when you hit the limit of your RAM.... then it suddenly becomes 7 times slower. Is there a way to predict which method is faster BEFORE doing the calculation? I guess it has something to do with the memory used by the variables and the available RAM.
Bruno Luong
Bruno Luong 2020 年 12 月 3 日
Divide the calculation into a chunks that do not exeed your PC ram, eg 8e4 images.

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