Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition

We propose in this paper to recognize human activities through an unsupervised learning of finite multivariate generalized Gaussian mixture model. We address an important cue in finite mixture model which is the estimation of the mixture model's parameters for a full covariance matrix. We have developed a novel learning algorithm based on Fixed-point covariance matrix estimator combined with the Expectation-Maximization algorithm. Furthermore, we have proposed an appropriate minimum message length (MML) criterion to deal with model selection problem. We evaluated our proposed method on synthetic datasets and a challenging application namely : Human activity recognition from images and videos. The obtained resutls show clearly the merits of our proposed framework which has better capabilities with full covariance matrix when modeling correlated data.