Large-scale gaussian process multi-class classification for semantic segmentation and facade recognition

This paper deals with the task of semantic segmentation, which aims to provide a complete description of an image by inferring a pixelwise labeling. While pixelwise classification is a suitable approach to achieve this goal, state-of-the-art kernel methods are generally not applicable since training and testing phase involve large amounts of data. We address this problem by presenting a method for large-scale inference with Gaussian processes. Standard limitations of Gaussian process classifiers in terms of speed and memory are overcome by pre-clustering the data using decision trees. This leads to a breakdown of the entire problem into several independent classification tasks whose complexity is controlled by the maximum number of training examples allowed in the tree leaves. We additionally propose a technique which allows for computing multi-class probabilities by incorporating uncertainties of the classifier estimates. The approach provides pixelwise semantics for a wide range of applications and different image types such as those from scene understanding, defect localization, and remote sensing. Our experiments are performed with a facade recognition application that shows the significant performance gain achieved by our method compared to previous approaches.