Regular Articles

Spatially variant mixture model for natural image segmentation

[+] Author Affiliations
Can Hu, Wentao Fan, Ji-Xiang Du, Nan Xie

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

J. Electron. Imaging. 26(4), 043005 (Jul 11, 2017). doi:10.1117/1.JEI.26.4.043005
History: Received April 5, 2017; Accepted June 19, 2017
Text Size: A A A

Abstract.  We tackle the problem of natural image segmentation by proposing a statistical approach that is based on spatially variant finite mixture models with generalized means. The contributions can be summarized as follows: first, the proposed spatially variant mixture model exploits beta-Liouville as basic distributions for describing the underlying data structure, which demonstrated better segmentation performance than commonly used distributions, such as Gaussian; second, the mixing proportions (i.e., the probabilities of class labels) in our model are modeled via the Dirichlet compound multinomial probability density, and the spatial smoothness is imposed by adopting the function of generalized mean over the mixture model as well as mixing proportions; and finally, a variational Bayes learning approach is developed to estimate model parameters and model complexity simultaneously with closed-form solutions. The robustness, accuracy, and effectiveness of the proposed model in image segmentation are demonstrated through experiments on both natural images and synthetic images degraded by noise compared with other state-of-the-art image segmentation methods.

Figures in this Article
© 2017 SPIE and IS&T

Citation

Can Hu ; Wentao Fan ; Ji-Xiang Du and Nan Xie
"Spatially variant mixture model for natural image segmentation", J. Electron. Imaging. 26(4), 043005 (Jul 11, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.4.043005


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

PubMed Articles
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.