Open AccessImage segmentation by Dirichlet process mixture model with generalised mean
Author(s) -
Zhang Hui,
Jonathan Wu Qing Ming,
Nguyen Thanh Minh
Publication year - 2014
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.2013.0232
Subject(s) - dirichlet distribution , artificial intelligence , pattern recognition (psychology) , image segmentation , segmentation , computer science , spatial analysis , posterior probability , bayesian probability , dirichlet process , mathematics , mixture model , statistics , mathematical analysis , boundary value problem
The Dirichlet process mixture model (DPMM) with spatial constraints – e.g. hidden Markov random field (HMRF) model – has been considered as an effective algorithm for image processing application. However, the HMRF model is complex and time‐consuming for implementation. A new DPMM has been introduced, where a generalised mean (GDM) is selected as the spatial constraints function. The GDM is applied not only on prior probability (and posterior probability) to incorporate local spatial information and component information, but also on conditional probability to incorporate local spatial information and observation information. The purpose of the HMRF model and GDM are the same for incorporating some spatial constraints into the system. However, compared to HMRF, GDM is easier, faster and simpler to implement. Finally, a variational Bayesian approach has been adopted for parameters estimation and model selection. Experimental results on image segmentation application demonstrate the improved performance of the proposed approach.