Image segmentation method based on self-organizing maps and K-means algorithm

In this paper, a method for color image segmentation based on Kohonenpsilas neural networks and clusterization by using modification of k-means algorithm, is presented. The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for...

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Bibliographic Details
Main Authors: Ristic, D.M., Pavlovic, M., Reljin, I.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Clustering algorithms
Color
Color image segmentation
Data mining
Image segmentation
Information retrieval
Iterative algorithms
K-means algorithm
Kohonen neural network
Neural networks
Pixel
Self organizing feature maps
self-organizing map
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Summary:In this paper, a method for color image segmentation based on Kohonenpsilas neural networks and clusterization by using modification of k-means algorithm, is presented. The method consists of three steps. First step includes usage of self-organizing maps for determination of potential candidates for regions centers. Secondly, using maxmin algorithm, number of candidates is reduced to initializing number of centers, which are then used for further analysis. During this process, initial number of regions is formed. For every formed region spatial and intensity centers are determined. Finally, in the third step, iterative procedure of modified k-means algorithm is realized during which the number of regions is minimized. The experimental results verify the usability of described algorithm.
DOI:10.1109/NEUREL.2008.4685551