As hinted in this answer, Perceptual hashing is a way to ascribe comparable measures (hashes) to images (rectangular patches). The images do not need to be the same size, and can be slightly distorted, according to the answer.
I might soon need a way to measurably describe image patches of different sizes, as well as irregular (not only different) shapes, and if Perceptual hashing could be adapted to work on non-rectangular image patches, it sounds interesting in this context.
I don't have any limitation to the way I describe the patches though, so any other suggestions are welcome. It does not have to be a distance measure, in other words, I just want a measure with a tendency to:
- be more similar between patches of similar shape
- be more similar between patches of similar content (similar subparts of the image)
- still tolerate slight distortion in shapes and pixel values
- can be but does not have to be more similar for shapes of similar size