Robust Student's-t mixture modelling via Markov random field and its application in image segmentation
by Taisong Xiong; Yuanyuan Huang; Xin Luo; Jing Zeng
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 11, No. 4, 2018

Abstract: Finite mixture model has been widely applied to image segmentation. However, the technique does not consider the spatial information in images that leads to unsatisfactory results for image segmentation. To address this problem, in this paper, a Student's-t mixture model is proposed for image segmentation based on Markov random field (MRF). There are three advantages in the proposed model. Firstly, a representation of spatial relationships among pixels is given. Secondly, Student's t-distribution is chosen to be the component function of the proposed model instead of the Gaussian distribution because of its heavy tail. Thirdly, to deduce the parameters of the proposed model, a gradient descent method is applied during the inference process. Comprehensive experiments are carried out on greyscale noisy images and real-world colour images. The experimental results have shown the effectiveness and robustness of the proposed model.

Online publication date: Tue, 24-Jul-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email