Evaluation of image segmentation by kohonen neural network and k-mean cluster algorithm
Author
Source
Issue
Vol. 45, Issue 1 (31 Dec. 2004), pp.209-212, 4 p.
Publisher
University of Baghdad College of Science
Publication Date
2004-12-31
Country of Publication
Iraq
No. of Pages
4
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
The present paper deals with image segmentation using Kohonen neural network and K-mean cluster approaches.
Image segmentation of the first technique has been implemented by using a single layer neural network trained by self-organization Kohonen competitive algorithm to produce a set of equiprobable weight vector.
These teehniques have been applied for three original images.
Kohonen approach gives better results with respect to K-mean cluster algorithm.
The reason for this enhanced result is that connectivity property between neighbouring in؛ the image is taken into consideration while its being neglected using k-means algorithm.
The connectivity property between neighbouring pixels is an important concept used in establishing boundaries of objects and components of regions in an image.
American Psychological Association (APA)
Ali, Nada Husayn Muhammad. 2004. Evaluation of image segmentation by kohonen neural network and k-mean cluster algorithm. Iraqi Journal of Science،Vol. 45, no. 1, pp.209-212.
https://search.emarefa.net/detail/BIM-596216
Modern Language Association (MLA)
Ali, Nada Husayn Muhammad. Evaluation of image segmentation by kohonen neural network and k-mean cluster algorithm. Iraqi Journal of Science Vol. 45, no. 1 (2004), pp.209-212.
https://search.emarefa.net/detail/BIM-596216
American Medical Association (AMA)
Ali, Nada Husayn Muhammad. Evaluation of image segmentation by kohonen neural network and k-mean cluster algorithm. Iraqi Journal of Science. 2004. Vol. 45, no. 1, pp.209-212.
https://search.emarefa.net/detail/BIM-596216
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 212
Record ID
BIM-596216