Genetic Programming for Feature Discovery and Image Discrimination

Abstract

: We apply Genetic Programming (GP) to the development of a processing tree for the classification of features extracted from images: measurements from a set of input nodes are weighted and combined through linear and nonlinear operations to form an output response. No constraints are placed upon size, shape, or order of processing within the network. This network is used to classify feature vectors extracted from IR imagery into target/nontarget categories using a database of 2000 training samples. Performance is tested against a separate database of 7000 samples. This represents a significant scaling up from the problems to which GP has been applied to date. Two experiments are performed: in the first set, we input classical "statistical" image features and minimize misclassification of target and non-target samples. In the second set of experiments, GP is allowed to form it's own feature set from primitive intensity measurements. For purposes of comparison, the same training and tes...