Open AccessHierarchical non‐parametric Markov random field for image segmentation
Author(s) -
Wang Xiangrong,
Zhao Jieyu
Publication year - 2017
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2016.0429
Subject(s) - markov random field , computer science , artificial intelligence , pattern recognition (psychology) , cluster analysis , markov chain , markov process , random field , markov chain monte carlo , image segmentation , parametric statistics , variable order markov model , markov model , hierarchical clustering , merge (version control) , bayesian probability , segmentation , machine learning , mathematics , statistics , information retrieval
Markov random fields (MRFs) are prominent in modelling image to handle image processing problems. However, they confront the bottleneck of model selection in further improving the performance. That is difficult to decide how many objects in an image automatically. Motivated by Bayesian non‐parametric (BN) models, a layered BN MRF is proposed. The proposed model is hierarchical: the lower level is a random‐field like model, while the higher level is a Chinese restaurant process (CRP). The clustering procedure can be formulated briefly as follows. The input data is first clustered with the lower level MRF to form a set of components. Then the higher level CRP is used to merge the components into larger clusters. Furthermore, a split–merge Monte Carlo Markov chain is employed. Quantitative evaluations over BSD500 data set and MSRC data set show the proposed model is comparable to the state‐of‐the‐art BN models and other graphical models in modelling unsupervised distance‐dependent problems.