Applying genetic programming to learn spatial differences between textures using a translation invariant representation

This paper describes an approach to evolving texture feature extraction programs using tree based genetic programming. The programs are evolved from a learning set of 13 textures selected from the Brodatz database. In the evolutionary phase, texture images are first "binarised" to 256 grey levels. An encoding of the positions of the black pixels is used as the input to the evolved programs. A separate feature extraction program is evolved for each of the 256 grey levels. Fitness is measured by applying the evolved program to all of the images in the learning set, using one dimensional clustering on the outputs and then using the separation between the clusters as the fitness value. On two benchmark problems using the evolved programs for feature extraction and a nearest neighbour classifier, the evolved features gave test accuracies of 74.6% and 66.2% respectively for a 13 Brodatz and a 15 Vistex texture problem. This is better than a number of human derived methods on the same problems.