Image segmentation by gaussian mixture models and modified FCM algorithm
Joint Authors
Kalti, Karim
Mahjub, Muhammad Ali
Source
The International Arab Journal of Information Technology
Issue
Vol. 11, Issue 1 (31 Jan. 2014)8 p.
Publisher
Publication Date
2014-01-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
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.
American Psychological Association (APA)
Kalti, Karim& Mahjub, Muhammad Ali. 2014. Image segmentation by gaussian mixture models and modified FCM algorithm. The International Arab Journal of Information Technology،Vol. 11, no. 1.
https://search.emarefa.net/detail/BIM-334148
Modern Language Association (MLA)
Kalti, Karim& Mahjub, Muhammad Ali. Image segmentation by gaussian mixture models and modified FCM algorithm. The International Arab Journal of Information Technology Vol. 11, no. 1 (Jan. 2014).
https://search.emarefa.net/detail/BIM-334148
American Medical Association (AMA)
Kalti, Karim& Mahjub, Muhammad Ali. Image segmentation by gaussian mixture models and modified FCM algorithm. The International Arab Journal of Information Technology. 2014. Vol. 11, no. 1.
https://search.emarefa.net/detail/BIM-334148
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-334148