A single metric for how similar two images are

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Note: this is a repost of one of my answers on SO.

This is a really 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 really great answer on this topic on SO.

You can use histogram comparision http://docs.opencv.org/doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.html

here is the code in python:

import cv

def compute_histogram(src, h_bins = 30, s_bins = 32):
    #create images
    hsv = cv.CreateImage(cv.GetSize(src), 8, 3)
    hplane = cv.CreateImage(cv.GetSize(src), 8, 1)
    splane = cv.CreateImage(cv.GetSize(src), 8, 1)
    vplane = cv.CreateImage(cv.GetSize(src), 8, 1)

    planes = [hplane, splane]
    cv.CvtColor(src, hsv, cv.CV_BGR2HSV)
    cv.Split(hsv, hplane, splane, vplane, None)

    #compute histogram  (why not use v_plane?)
    hist = cv.CreateHist((h_bins, s_bins), cv.CV_HIST_ARRAY,
            ranges = ((0, 180),(0, 255)), uniform = True)
    cv.CalcHist(planes, hist)      #compute histogram
    cv.NormalizeHist(hist, 1.0)    #normalize hist

    return hist

src1 = cv.LoadImage("C:/ICP/buterfly_0.jpg", cv.CV_LOAD_IMAGE_COLOR)
src2 = cv.LoadImage("C:/ICP/buterfly_1.jpg", cv.CV_LOAD_IMAGE_COLOR)
hist1= compute_histogram(src1)
hist2= compute_histogram(src2)
sc= cv.CompareHist(hist1, hist2, cv.CV_COMP_BHATTACHARYYA)
print sc