Genetic programming for edge detection using multivariate density

Wenlong Fu, Mark Johnston, Mengjie Zhang

Abstract: The combination of local features in edge detection can generally improve detection performance. However, how to effectively combine different basic features remains an open issue and needs to be investigated. Multivariate density is a generalisation of the one-dimensional (univariate) distribution to higher dimensions. In order to effectively construct composite features with multivariate density, a Genetic Programming (GP) system is proposed to evolve Bayesian-based programs. An evolved Bayesian-based program estimates the relevant multivariate density to construct a composite feature. The results of the experiments show that the GP system constructs high-level combined features which substantially improve the detection performance.