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Mixture Models and Applications pp 81–106Cite as

Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions

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Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

The huge amount of data expanding day by day entail creating powerful real-time algorithms. Such algorithms allow a reactive processing between the input multimedia data and the system. In particular, we are mainly concerned with active learning and clustering images and videos for the purpose of pattern recognition. In this paper, we propose a novel online recognition algorithm based on multivariate generalized Gaussian distributions. We estimate at first the generative model’s parameters within a discriminative framework (fixed-point, Riemannian averaged fixed-point, and Fisher scoring). Then, we propose an online recognition algorithm in accordance with those algorithms. Finally, we applied our proposed framework on three challenging problems, namely: human action recognition, facial expression recognition, and pedestrian detection from infrared images. Experiments demonstrate the robustness of our approach by comparing with the state-of-the art algorithms and offline learning techniques.

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Author information

Authors and Affiliations

  1. Laboratoire RISC Robotique Informatique et Systèmes Complexes, Université de Tunis El Manar, ENIT, Tunis, Tunisia

    Fatma Najar

  2. Taif University, Taif, Saudi Arabia

    Sami Bourouis

  3. Université de Tunis El Manar, LR-SITI Laboratoire Signal, Image et Technologies de l’Information, Tunis, Tunisia

    Sami Bourouis

  4. Gulf College, Al Maabelah, Muscat, Oman

    Rula Al-Azawi & Ali Al-Badi

Authors
  1. Fatma Najar
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  2. Sami Bourouis
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  3. Rula Al-Azawi
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  4. Ali Al-Badi
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Corresponding author

Correspondence to Fatma Najar .

Editor information

Editors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Nizar Bouguila

  2. Department of Computer Science and Technology, Huaqiao University, Xiamen, China

    Wentao Fan

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Najar, F., Bourouis, S., Al-Azawi, R., Al-Badi, A. (2020). Online Recognition via a Finite Mixture of Multivariate Generalized Gaussian Distributions. In: Bouguila, N., Fan, W. (eds) Mixture Models and Applications. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-23876-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-23876-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23875-9

  • Online ISBN: 978-3-030-23876-6

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