Can I use convolutional filters used on 32x32 px size images upon 600x800px images?
If the features are the same sizes (aproximately) then zero pad the edges and go to town. If not, then there are tricks you can do that will allow them to be used with slightly more noisy results.
- sub-sample the "big" pictures so they are on the scale of the
learning. Convolutional methods work on sub-pixel resolutions.
Sub-sampling can be "pick random", "pick mean", "pick median" or
other. Personally I like to pick about 30% of the way between the
mean and the median, but it is just personal preference.
- interpolate rows and columns in the convolutional templates, so that
they operate as if they were larger. Zero padding rows/columns can
The convolutional templates are invariant on translation, not the other operations (rotation, skew, scale).
I think there are higher-dimensional analogies to the FFT, cousins and second cousins, that are invariant in those other operations, but I do not know what they are and they are higher dimensional. From my "mathematical modeling" undergraduate course sponsored by Lockheed, I am sure that whatever they are - they are of interest for folks who perform extensive photogrammetry. Those analogies would work seamlessly without having to account for rotation, skew or scale.