Spatially Constrained Non-Gaussian Mixture Model for Image Segmentation

被引：0

作者：

Singh, Jai Puneet ^{[1
]}

Bouguila, Nizar ^{[1
]}

机构：

[1] Concordia Univ, CIISE, Montreal, PQ, Canada

来源：

2017 IEEE 30TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | 2017年

关键词：

Image Segmentation; Dirichlet; Mixture Model; Markov Random Field; EM ALGORITHM;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A new color image segmentation algorithm based on spatial information with the Dirichlet mixture model is presented. This method uses Markov Random Field to incorporate spatial information between neighboring pixels into a Dirichlet mixture model. The segmentation model is learned using Expectation Maximization (EM) algorithm based on Newton Raphson step. The obtained results using real image data set are more encouraging than those obtained using similar approaches.

引用

页数：4

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