Regular Articles

Finite general Gaussian mixture modeling and application to image and video foreground segmentation

[+] Author Affiliations
Mohand Saïd Allili

University of Sherbrooke, Department of Computer Science, Faculty of Science, Sherbrooke, J1K 2R1, Quebec, Canada

Nizar Bouguila

Concordia University, Concordia Institute for Information Systems Engineering, 1515 Saint Catherine Street West, EV007.632, Montreal H3G 2W1, Quebec, Canada

Djemel Ziou

University of Sherbrooke, Department of Computer Science, Faculty of Science, Sherbrooke, J1K 2R1, Quebec, Canada

J. Electron. Imaging. 17(1), 013005 (March 28, 2008). doi:10.1117/1.2898125
History: Received September 04, 2006; Revised April 26, 2007; Accepted July 16, 2007; Published March 28, 2008
Text Size: A A A

We propose a new finite mixture model based on the formalism of general Gaussian distribution (GGD). Because it has the flexibility to adapt to the shape of the data better than the Gaussian, the GGD is less prone to overfitting the number of mixture classes when dealing with noisy data. In the first part of this work, we propose a derivation of the maximum likelihood estimation for the parameters of the new mixture model, and elaborate an information-theoretic approach for the selection of the number of classes. In the second part, we validate the proposed model by comparing it to the Gaussian mixture in applications related to image and video foreground segmentation.

Figures in this Article
© 2008 SPIE and IS&T

Citation

Mohand Saïd Allili ; Nizar Bouguila and Djemel Ziou
"Finite general Gaussian mixture modeling and application to image and video foreground segmentation", J. Electron. Imaging. 17(1), 013005 (March 28, 2008). ; http://dx.doi.org/10.1117/1.2898125


Tables

Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.