The EM/MPM algorithm for segmentation of textured images: Analysis and further experimental results

被引：102

作者：

Comer, ML ^{[1
]}

Delp, EJ

机构：

[1] Thomson Consumer Elect, Indianapolis, IN 46206 USA

[2] Purdue Univ, Video & Image Proc Lab, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 2000年 / 9卷 / 10期

基金：

美国国家科学基金会;

关键词：

expectation-maximization (EM) algorithm; maximization of the posterior marginals (MPM) algorithm; segmentation;

D O I：

10.1109/83.869185

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 new results relative to the "expectation-maximization/maximization of the posterior marginals" (EM/MPM) algorithm for simultaneous parameter estimation and segmentation of textured images. The EM/MPM algorithm uses a Markov random field model for the pixel class labels and alternately approximates the MPM estimate of the pixel class labels and estimates parameters of the observed image model. The goal of the EM/MPM algorithm is to minimize the expected value of the number of misclassified pixels. We present new theoretical results in this paper which show that the algorithm can be expected to achieve this goal, to the extent that the EM estimates of the model parameters are close to the true values of the model parameters. We also present new experimental results demonstrating the performance of the EM/MPM algorithm.

引用

页码：1731 / 1744

页数：14

共 15 条

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