Image segmentation by Dirichlet process mixture model with generalised mean
Hui Zhang, Q. M. Jonathan Wu, Thanh Minh Nguyen · IET Image Processing · 2014
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.