image comparison

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This is a huge topic, with answers from 3 lines of code to entire research magazines.

I will outline the most common such techniques and their results.

• Comparing histograms. One of the simplest & fastest methods. Proposed decades ago as a means to find picture simmilarities. The idea is that a forest will have a lot of green, and a human face a lot of pink, or whatever. So, if you compare two pictures with forests, you'll get some simmilarity between histograms, because you have a lot of green in both.

  Downside: it is too simplistic. What is the difference between a banana and a beach? both are yellow.

  OpenCV method: compareHist()

• Template matching : http://stackoverflow.com/questions/8520882/matchtemplate-finding-good-match. It convolutes the search image with the one being search into. It is usually used to find smaller image parts in a bigger one.

  Downsides: It only returns good results with identical images, same size & orientation.
  OpenCV method: matchTemplate()

• Feature matching. Considered one of the most efficient ways to do image search. A number of features are extracted from an image, in a way that guarantees the same features will be recognized again even it is rotated/scaled/skewed. The features extracted this way can be matched against other image feature sets. Another image that has a high proportion of the features in the first one is most probably depicting the same object/scene.

  There are a number of OpenCV tutorials/samples on this, and a nice video here. A whole OpenCV module (features2d) is dedicated to it.
  Downsides: It may be slow. It is not perfect.

And here is a great answer on this topic: http://stackoverflow.com/questions/23931/what-algorithm-to-compare-two-images

[cross-posted from SO]

you can use a histogram comparison: http://docs.opencv.org/doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.html

or PSNR/MSSIM: http://docs.opencv.org/doc/tutorials/highgui/video-input-psnr-ssim/video-input-psnr-ssim.html

or you can detect keypoints from both images, compare them and count the number of "good" matches, if the number is high enough it is probably a match