Generation of feature detectors for texture discrimination by genetic search

Abstract

A genetic-algorithm-based methodology for learning a set of feature detectors for texture discrimination is presented. This methodology is incorporated into a vision system that learns to classify noisy examples of different texture classes by evolving populations of simple and texture-specific local spatial feature detectors. The results of preliminary work confirm the utility of the genetic algorithm in solving problems within the specified domain.<>