Image comparison algorithm

Question

I'm trying to compare images to each other to find out whether they are different. First I tried to make a Pearson correleation of the RGB values, which works also quite good unless the pictures are a litte bit shifted. So if a have a 100% identical images but one is a little bit moved, I get a bad correlation value.

Any suggestions for a better algorithm?

BTW, I'm talking about to compare thousand of imgages...

Edit: Here is an example of my pictures (microscopic):

im1:

im2:

im3:

im1 and im2 are the same but a little bit shifted/cutted, im3 should be recognized as completly different...

Edit: Problem is solved with the suggestions of Peter Hansen! Works very well! Thanks to all answers! Some results can be found here http://labtools.ipk-gatersleben.de/image%20comparison/image%20comparision.pdf

Answer 1

A similar question was asked a year ago and has numerous responses, including one regarding pixelizing the images, which I was going to suggest as at least a pre-qualification step (as it would exclude very non-similar images quite quickly).

There are also links there to still-earlier questions which have even more references and good answers.

Here's an implementation using some of the ideas with Scipy, using your above three images (saved as im1.jpg, im2.jpg, im3.jpg, respectively). The final output shows im1 compared with itself, as a baseline, and then each image compared with the others.

>>> import scipy as sp >>> from scipy.misc import imread >>> from scipy.signal.signaltools import correlate2d as c2d >>> >>> def get(i): ...     # get JPG image as Scipy array, RGB (3 layer) ...     data = imread('im%s.jpg' % i) ...     # convert to grey-scale using W3C luminance calc ...     data = sp.inner(data, [299, 587, 114]) / 1000.0 ...     # normalize per http://en.wikipedia.org/wiki/Cross-correlation ...     return (data - data.mean()) / data.std() ... >>> im1 = get(1) >>> im2 = get(2) >>> im3 = get(3) >>> im1.shape (105, 401) >>> im2.shape (109, 373) >>> im3.shape (121, 457) >>> c11 = c2d(im1, im1, mode='same')  # baseline >>> c12 = c2d(im1, im2, mode='same') >>> c13 = c2d(im1, im3, mode='same') >>> c23 = c2d(im2, im3, mode='same') >>> c11.max(), c12.max(), c13.max(), c23.max() (42105.00000000259, 39898.103896795357, 16482.883608327804, 15873.465425120798) 

So note that im1 compared with itself gives a score of 42105, im2 compared with im1 is not far off that, but im3 compared with either of the others gives well under half that value. You'd have to experiment with other images to see how well this might perform and how you might improve it.

Run time is long... several minutes on my machine. I would try some pre-filtering to avoid wasting time comparing very dissimilar images, maybe with the "compare jpg file size" trick mentioned in responses to the other question, or with pixelization. The fact that you have images of different sizes complicates things, but you didn't give enough information about the extent of butchering one might expect, so it's hard to give a specific answer that takes that into account.