Color Image Segmentation with a Hyper-Conic Multilayer Perceptron

摘要

We apply the Hyper-Conic Artificial Multilayer Perceptron (HC-MLP) to color image segmentation, where we consider image segmentation as a classification problem distinguishing between foreground and background pixels. The HC-MLP was designed by using the conic space and conformal geometric algebra. The neurons in the hidden layer contain a transfer function that defines a quadratic surface (spheres, ellipsoids, paraboloids and hyperboloids) by means of inner and outer products, and the neurons in the output layer contain a transfer function that decides whether a point is inside or outside a sphere. The Particle Swarm Optimization algorithm (PSO) is used to train the HC-MLP. A benchmark of fifty images is used to evaluate the performance of the algorithm and compare our proposal against statistical methods which use copula gaussian functions.