By Topic

A novel image segmentation algorithm based on Hidden Markov Random Field model and Finite Mixture Model parameter estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Kai Hu ; Key Lab. of Intell. Comput. & Inf. Process. of Minist. of Educ., Xiangtan Univ., Xiangtan, China ; Guang-Yu Tang ; Da-Peng Xiong ; Quan Qiu

Hidden Markov Random Field (HMRF) model and Finite Mixture Model (FMM) parameter estimation algorithm provides an interesting framework for image segmentation task, hence a technique that capitalizes on the benefits of both algorithms would achieve better performance. In this regard, we propose a new segmentation algorithm which combines with HMRF model and FMM parameter estimation algorithm. Firstly, we use a real-coded genetic algorithm based FMM to estimate image parameters. Secondly, according to the estimated image parameters, image pixels are classified into different classes through the HMRF segmentation framework. The performance of the proposed algorithm is tested on Berkeley image segmentation dataset. Experimental results have confirmed that the proposed algorithm offers a useful improvement of the segmentation accuracy over competing methodologies.

Published in:

Wavelet Analysis and Pattern Recognition (ICWAPR), 2012 International Conference on

Date of Conference:

15-17 July 2012