Man-Made Structure Segmentation using Gaussian Processes and Wavelet Features

Abstract

We apply Gaussian process classification (GPC) to man-made structure segmentation, treated as a two class problem. GPC is a discriminative approach, and thus focuses on modelling the posterior directly. It relaxes the strong assumption of conditional independence of the observed data (generally used in a generative model). In addition, wavelet transform features, which are effective in describing directional textures, are incorporated in the feature vector. Satisfactory results have been obtained which show the effectiveness of our approach.