Open AccessNon‐parametric mixture model with TV spatial regularisation and its dual expectation maximisation algorithm
Author(s) -
Yan Shi,
Yu Zihao,
Liu Jun
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1251
Subject(s) - robustness (evolution) , computer science , parametric statistics , algorithm , markov random field , kernel (algebra) , image segmentation , segmentation , kernel density estimation , artificial intelligence , expectation–maximization algorithm , estimator , mixture model , parametric model , dual (grammatical number) , pattern recognition (psychology) , mathematics , maximum likelihood , statistics , art , biochemistry , chemistry , literature , combinatorics , gene
An image segmentation method based on a non‐parametric mixture model together with total variation (TV) regularisation is proposed. The authors use a kernel density estimator as a basic mixture model, which can better separate the non‐central distributed data. To enforce its robustness, they integrate the well‐known TV regularisation into the statistical method. They use the dual method to efficiently solve the TV‐related energy and get a new dual expectation maximisation algorithm. Experiments on both synthetic images and real images show that the proposed algorithm can achieve good segmentation results. Compared with the parametric models and hidden Markov random field‐based method, the proposed method can produce better result in some cases.