How can I measure the similarity between two images?

The 'classic' way of measuring this is to break the image up into some canonical number of sections (say a 10x10 grid) and then computing a histogram of RGB values inside of each cell and compare corresponding histograms. This type of algorithm is preferred because of both its simplicity and it's invariance to scaling and (small!) translation.

This depends entirely on how smart you want the algorithm to be.

For instance, here are some issues:

• cropped images vs. an uncropped image
• images with a text added vs. another without
• mirrored images

The easiest and simplest algorithm I've seen for this is just to do the following steps to each image:

1. scale to something small, like 64x64 or 32x32, disregard aspect ratio, use a combining scaling algorithm instead of nearest pixel
2. scale the color ranges so that the darkest is black and lightest is white
3. rotate and flip the image so that the lighest color is top left, and then top-right is next darker, bottom-left is next darker (as far as possible of course)

Edit A combining scaling algorithm is one that when scaling 10 pixels down to one will do it using a function that takes the color of all those 10 pixels and combines them into one. Can be done with algorithms like averaging, mean-value, or more complex ones like bicubic splines.

Then calculate the mean distance pixel-by-pixel between the two images.

To look up a possible match in a database, store the pixel colors as individual columns in the database, index a bunch of them (but not all, unless you use a very small image), and do a query that uses a range for each pixel value, ie. every image where the pixel in the small image is between -5 and +5 of the image you want to look up.

This is easy to implement, and fairly fast to run, but of course won't handle most advanced differences. For that you need much more advanced algorithms.

Use a normalised colour histogram. (Read the section on applications here), they are commonly used in image retrieval/matching systems and are a standard way of matching images that is very reliable, relatively fast and very easy to implement.

Essentially a colour histogram will capture the colour distribution of the image. This can then be compared with another image to see if the colour distributions match.

This type of matching is pretty resiliant to scaling (once the histogram is normalised), and rotation/shifting/movement etc.

Avoid pixel-by-pixel comparisons as if the image is rotated/shifted slightly it may lead to a large difference being reported.

Histograms would be straightforward to generate yourself (assuming you can get access to pixel values), but if you don't feel like it, the OpenCV library is a great resource for doing this kind of stuff. Here is a powerpoint presentation that shows you how to create a histogram using OpenCV.