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Image Segmentation using Isodata Clustering with Parameters Estimated by Evolutionary Approach: Application to Quality Control

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 66 - Number 19
Year of Publication: 2013
Authors:
M. Merzougui
M. Nasri
B. Bouali
10.5120/11194-6345

M Merzougui, M Nasri and B Bouali. Article: Image Segmentation using Isodata Clustering with Parameters Estimated by Evolutionary Approach: Application to Quality Control. International Journal of Computer Applications 66(19):25-30, March 2013. Full text available. BibTeX

@article{key:article,
	author = {M. Merzougui and M. Nasri and B. Bouali},
	title = {Article: Image Segmentation using Isodata Clustering with Parameters Estimated by Evolutionary Approach: Application to Quality Control},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {66},
	number = {19},
	pages = {25-30},
	month = {March},
	note = {Full text available}
}

Abstract

A segmentation method based on pixel classification by Isodata algorithm and evolution strategies is proposed in this paper. The Isodata algorithm is an unsupervised data classification algorithm. Its result depends strongly on two parameters: distance threshold for the union of clusters and threshold of typical deviation for the division of a cluster. A bad choice of these two parameters leads the algorithm to spiral out of control leaving the end only one class. To determine these parameters and improvements to this algorithm, evolution strategies are used. An evolutionary algorithm is adapted to estimate the two optimal thresholds to be used by the algorithm then Isodata. To note that the other parameters are chosen empirically. The application of this evolutionary method (Evolutionary Isodata: EIsodata) on synthetic and real images helps to validate this approach and show its interest in the problem of decision support in the quality control.

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