Over-Segmentation of VHR Satellite Images Using Nonparametric Bayesian Iterative Clustering
Wei Huang, Hong Tang, Xin Yang · 2019
Over-segmentation has often been employed to simplify representation of images and speed the process of image analysis, where each over-segment is also called a superpixel. However, the superpixels generated by the existing algorithms rely heavily on some preset parameters, for example, number of superpixels. In this paper, we present a novel superpixel algorithm under the framework of nonparametric Bayesian image clustering, which is called Nonparametric Bayesian Iterative Clustering (NBIC). Unlike traditional approaches for image clustering, Bayesian nonparametric models provide a principled way to infer the number of clusters from observed data. Therefore, unlike traditional approach to image over-segmentation, the NBIC is free of preset number of superpixels. The NBIC to superpixel segmentation is based on histogram clustering, and local histograms are extracted as observation. For each observation, compute the probability of each class and sample a class label accordingly. The cluster probabilities depend on the number of classes in the pixel neighborhood and the distance of cluster center. This method groups pixels into meaningful regions without setting any parameters related to superpixels in the nonparametric Bayesian theory. The adaptive amount of superpixels can eventually be converged, and the size of superpixels can automatically be controlled by iterative calculation