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

  • Fatma NajarEmail author
  • Sami Bourouis
  • Nizar Bouguila
  • Safya Belghith


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.


Multivariate generalized Gaussian Mixture models Covariance matrix estimation Minimum message length Human activity recognition 



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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Fatma Najar
    • 1
    Email author
  • Sami Bourouis
    • 2
    • 3
  • Nizar Bouguila
    • 4
  • Safya Belghith
    • 1
  1. 1.Université de Tunis El Manar, ENIT, Laboratoire RISC Robotique Informatique et Systèmes ComplexesTunisTunisia
  2. 2.LR-SITI Laboratoire SignalImage et Technologies de l’Information TunisTunisTunisia
  3. 3.Taif UniversityTaifSaudi Arabia
  4. 4.The Concordia Institute for Information Systems Engineering (CIISE)Concordia UniversityMontrealCanada

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