Fusion of hidden Markov random field models and its Bayesian estimation

被引：14
|
作者：
Destrempes, Francois
Angers, Jean-Francois
Mignotte, Max
机构：
[1] Univ Montreal, Dept Informat & Rech Operat, Montreal, PQ H3C 3J7, Canada
[2] Univ Montreal, Dept Math & Stat, Montreal, PQ H3C 3J7, Canada
来源：
IEEE TRANSACTIONS ON IMAGE PROCESSING | 2006年 / 15卷 / 10期
关键词：
bayesian estimation; color and texture segmentation; Exploration/Selection algorithm; Exploration/Selection/Estimation procedure; fusion of hidden Markov random field models; Julesz ensembles; Markov Chain Monte Carlo (MCM) algorithm;
D O I：
10.1109/TIP.2006.877522
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present a Hidden Markov Random Field (HMRF) data-fusion model. The proposed model is applied to the segmentation of natural images based on the fusion of colors and textons into Julesz ensembles. The corresponding Exploration/ Selection/Estimation (ESE) procedure for the estimation of the parameters is presented. This method achieves the estimation of the parameters of the Gaussian kernels, the mixture proportions, the region labels, the number of regions, and the Markov hyper-parameter. Meanwhile, we present a new proof of the asymptotic convergence of the ESE procedure, based on original finite time bounds for the rate of convergence.
引用
收藏
页码：2920 / 2935
页数：16
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