Robust Student's-t mixture modelling via Markov random field and its application in image segmentation. (2018)

• Record Type: Journal Article
• Authors: Xiong, Taisong; Huang, Yuanyuan; Luo, Xin; Zeng, Jing
• Is Part Of: International journal of high performance computing and networking. Volume 11:Number 4(2018)
• Page Start: 342
• Page End: 350
• Publication Date: 2018
• Subjects: Student's-t mixture model -- Markov random field -- image segmentation -- gradient descent; High performance computing -- Periodicals; Computer networks -- Periodicals; High performance computing; Periodicals; 004.05
• Languages: English
• ISSNs: 1740-0562

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.