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copied!<p>Yikes! I beat my head against this one many years ago and never came up with a viable answer for my problem. I have had some thoughts since that I haven't tried to implement and I'll throw them out just in case they do you any good:</p> <p>I disagree with Al Crowley's statement about the average RGB values--the cropping could very well be the removal of an object that was quite different than the target.</p> <p>Furthermore, an observation: Reducing images to an 8x8 thumbnail (not that it's big enough for human eyes) and taking the square of the difference of each component does a pretty good job of picking out duplicates and often works even with small crops around the edge. Ideally contrast should be normalized before doing the thumbnail, I didn't do it and found it was declaring a bunch of low-contrast images to be dupes of each other when they had no similarity at all. This approach uses only 192 bytes per image and thus allows you to compare a large number of images in memory although it's still an O(n^2) algorithm. (I made it so you could close the program at any time, it would dump the state info and shut down, next time it would resume where it left off.)</p> <p>Given what you are describing as a problem I would be inclined to try taking a set of test crops of the image and running the comparison against each crop. When you find a likely candidate then start working with bigger images and try to narrow it down by jogging a few pixels and seeing if it gets better or worse. I can't imagine anything other than very slow performance from such a routine.</p>

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