A Fixed-Point Estimation Algorithm for Learning the Multivariate GGMM: Application to Human Action Recognition

Abstract

Multivariate generalized Gaussian distribution has been an attractive solution to many signal and image processing applications. Therefore, efficient estimation of its parameters is of significant interest for a number of research problems. The main contribution of this paper is to develop a fixed-point estimation algorithm for learning the multivariate generalized Gaussian mixture model's parameters (MGGMM). A challenging application that concerns Human action recognition is deployed to validate our statistical framework and to show its merits.