Can I compute Haar-features using "Coefficients" in a IntegralKernel defined filter instead of integralFilter() function?

Question

I define a Haar-like feature filter H by the IntegralKernel class in Matlab.

H = integralKernel(integralKernel([1,1,im_width,im_height;...
                                   2, 2, w, h;...
                                   2+w, 2, w, h;],...
                                   [0,1,-1]);

im_width and im_height are width and height of the image; w and h are width and height of the bounding box of the filter. The returned filte H is a Matlab struct type variable, which contains the fields like:

BoundingBoxes
Weights
Coefficients - Conventional filter coefficients.
Center
Size

In Matlab document, it uses the function integralFilter(IntegralImage, H), when computing the feature from the given integral image. The code is like:

HaarFeature = integralFilter(integralImage(I), H)

But this takes a long time if the number of images are large (e.g., 10,000 images), because I need to use a for-loop to compute every image. However, I found that is much quicker if I just compute the sum of the given original image I and the coefficients in the filer H. The code is like:

HaarFeature = sum(sum(I .* H.Coefficients));

Both I and H.Coefficients are double type in Matlab.

My question is that if these two ways to compute the Haar Features are equivalent? Thanks!

Answer 1

Do you have to filter each image with multiple kernels? In that case you should only call integralImage(I) once for each image, and store and re-use the result. Computing the integral image takes some time. However, once you have it, applying an integral kernel to the resulting integral image should be very fast.