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Computer Science: Evaluation of Image Segmentation by Kohonen Neural Network and K-Mean Cluster Algorithm

 

Evaluation of Image Segmentation by Kohonen Neural Network and K-Mean Cluster Algorithm

Nada H. Mohammad Ali

Department of Computer Science, Collage of Science, University of Baghdad. Baghdad-lraq.

Abstract

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 techniques 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.


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