Image Segmentation by Gaussian Mixture Models and Modified FCM Algorithm
Journal: The International Arab Journal of Information Technology (Vol.11, No. 1)Publication Date: 2014-01-01
Authors : Karim Kalti; Mohamed Mahjoub;
Page : 11-18
Keywords : EM algorithm; FCM algorithm; image segmentation; adaptive distance;
Abstract
The Expectation Maximization (EM) algorithm and the clustering method Fuzzy-C-Means (FCM) are widely used in image segmentation. However, the major drawback of these methods is their sensitivity to the noise. In this paper, we propose a variant of these methods which aim at resolving this problem. Our approaches proceed by the characterization of pixels by two features: the first one describes the intrinsic properties of the pixel and the second characterizes the neighborhood of pixel. Then, the classification is made on the base on adaptive distance which privileges the one or the other features according to the spatial position of the pixel in the image. The obtained results have shown a significant improvement of our approaches performance compared to the standard version of the EM and FCM, respectively, especially regarding about the robustness face to noise and the accuracy of the edges between regions.
Other Latest Articles
- A Survey: Face Recognition Techniques under Partial Occlusion
- Semantic Boolean Arabic Information Retrieval
- A Human Activity Recognition System using HMMs with GDA on Enhanced Independent Component Features
- Stability Coalition Formation with Cost Sharing in Multi-Agent Systems Based on Volume Discount
- A Comparative Analysis of Software Protection Schemes
Last modified: 2019-11-17 18:40:16