Unsupervised clustering of depth images using Watson mixture model
Abstract
In this paper, we propose an unsupervised clustering method for axially symmetric directional unit vectors. Our method exploits the Watson distribution and Bregman Divergence within a Model Based Clustering framework. The main objectives of our method are: (a) provide efficient solution to estimate the parameters of a Watson Mixture Model (WMM); (b) generate a set of WMMs and (b) select the optimal model. To this aim, we develop: (a) an efficient soft clustering method; (b) a hierarchical clustering approach in parameter space and (c) a model selection strategy by exploiting information criteria and an evaluation graph. We empirically validate the proposed method using synthetic data. Next, we apply the method for clustering image normals and demonstrate that the proposed method is a potential tool for analyzing the depth image.