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

Fatma Naiar; Sami Bourouis; Nizar Bouguila; Safya Belghith

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

Fatma Naiar, Sami Bourouis, Nizar Bouguila and Safya Belghith

The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.1

01/01/2018

Abstract

Algorithms

Gaussian distribution

Human motion

Image processing

Machine learning

Moving object recognition

Normal distribution

Parameter estimation

Probabilistic models

Recognition

Signal processing

Statistical analysis

Conference Title: 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) Conference Start Date: 2018, May 13 Conference End Date: 2018, May 16 Conference Location: Quebec, QC, Canada 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.

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Title: A Fixed-Point Estimation Algorithm for Learning the Multivariate GGMM: Application to Human Action Recognition
Creators - without role: Fatma Naiar
Sami Bourouis
Nizar Bouguila
Safya Belghith
Publication Details: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.1
Publisher: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Identifiers: 9910797008331
Academic Unit: Taif University
Language: English
Resource Type: Conference proceeding