A comparison between different Gaussian-based Mixture Models

In this paper, we address the problem of data clustering into homogeneous components in an unsupervised way. Data clustering is one of the major topics in computer vision which has widespread potential applications from various domains such as pattern recognition, data mining, remote sensing, and bioinformatics. In pattern recognition, statistical methods have been widely used and proved effective in generating accurate models. In particular, the popular finite Gaussian mixture models which are able to provide superior performance for data clustering and classification. In this work, we present and evaluate the performance of four well-known Gaussian-based mixture models for data clustering namely: Gaussian mixture model (GMM), Generalized Gaussian mixture model (GGMM), Bounded Gaussian mixture model (BGMM) and Bounded Generalized Gaussian mixture model (BGGMM). The aim of this work is to show that the choice of the component model is very critical in mixture decomposition. Experimental results show close clustering accuracy between different models. However, the bounded generalized Gaussian mixture model provides the best performance in the case of multidimensional data.