I'm developing correlation filters based image recognition.
I implemented MACE correlation filter in matlab:

training code:

D = diag(mean(abs(X),2));
% inv(A) * B = A \ B
XDX = ctranspose(X) * (D \ X);
h = (D \ X) * (XDX \ u);
H = reshape(h, size(I));

where X is the d.^2xN matrix of vectorized FFT2 transformed training images (images FFT2 transform spectra are vectorized by concatenating columns), u is the vector of image membership (1 for images to recognize and 0 for the rest of the samples), I is the dxd sample training image to reshape correlation filter h to H matrix.

testing code:

R = ifftshift(ifft2(I.*conj(H)));

where I is the dxd test image zero mean and unit variance normalized and H is the trained correlation filter.

I trained one correlation filter on 61 training images from which there is just 1 image to recognize with 1 in vector u and remaining images with 0's in vector u.

However despite reported results in the papers on correlation filters where correlation plane output is in range 0 ... 1 my results are up to 10e-4.

correlation plane surfaces for recognized and unrecognized images

Is there any normalization typically applied to scale correlation plane output?

Besides I found that without conj(H) in the testing code there is no correlation peak for images to be recognized. I was unable to find in the papers that missing operator in the formula which I found at mathworks forum


1 Answer 1


I was just working on the same issue and the magnitude was the same as yours (10e-4), but the peak in the middle, in terms of the shape, looked correct.

I was able to solve this problem by scaling back R, by d, the number of pixels in the image. In my case I am using 64 x 64 pixel images (d = 64*64 = 4096).

Also, make sure you take the real of R, to get rid of the complex part, even though it should be very small, then multiply R by d and your scaling should be correct. To test this, use an image you trained your filter on and your max(R(:)*d) = 1.0.

R = ifftshift(ifft2(test_img.*conj(H_square)));
R = real(R);
axis([0 64 0 64 -0.5 1]);

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