Abstract

The aim of the study is to present an image segmentation method based on feature space clustering with the Gaussian Mixture Model (GMM) and the Expectation Maximization (EM) algorithm. To address the issue of selection of determining the number of clusters, the Bayesian Ying-Yang (BYY) learning is employed. Moreover, to improve the performance of the segmentation on noisy images, both intensity and spatial position information are employed as features to describe a pixel in the image. The simulation results on images with and without noises validate the performance of the proposed method.

Keywords:

Bayesian ying-yang learning, clusters, feature space, images segmentation,

References

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.