Universal distance measure for images
Uzi Chester, Joel Ratsaby · 2012
We introduce an algorithm for measuring the distance between two images based on computing the complexity of two strings of characters that encode the images. Given a pair of images, our algorithm transforms each one into a text-based sequence (strings) of characters. For each string, it computes the LZ-complexity and then uses the string-distance measure of [1] to obtain a distance value between the images. The main advantages of our algorithm are that it is universal, that is, it neither needs nor assumes any spatial or spectral information about the images, it can measure the distance between two images of different sizes, it works for black and white, grayscale and color images, and it can be implemented efficiently on an embedded computer system. We present successful experimental results on clustering images of different sizes into categories based on their similarities as measured by our algorithm.