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

by M. Merzougui, M. Nasri, B. Bouali
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 19
Year of Publication: 2013
Authors: M. Merzougui, M. Nasri, B. Bouali
10.5120/11194-6345

M. Merzougui, M. Nasri, B. Bouali . Image Segmentation using Isodata Clustering with Parameters Estimated by Evolutionary Approach: Application to Quality Control. International Journal of Computer Applications. 66, 19 ( March 2013), 25-30. DOI=10.5120/11194-6345

@article{ 10.5120/11194-6345,
author = { M. Merzougui, M. Nasri, B. Bouali },
title = { Image Segmentation using Isodata Clustering with Parameters Estimated by Evolutionary Approach: Application to Quality Control },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 19 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number19/11194-6345/ },
doi = { 10.5120/11194-6345 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:22:53.600379+05:30
%A M. Merzougui
%A M. Nasri
%A B. Bouali
%T Image Segmentation using Isodata Clustering with Parameters Estimated by Evolutionary Approach: Application to Quality Control
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 19
%P 25-30
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
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.

References
  1. Cocquerez T. P. et Phillip S. 'Analyse d'images : Filtrage et segmentation'. Editions MASSON, Paris, 1995.
  2. Nasri M. 'Contribution à la classification de données par Approches Evolutionnistes : Simulation et Application aux images de textures'. Thèse de doctorat. Université Mohammed premier Oujda 2004.
  3. M. Nasri, M. EL Hitmy, H. Ouariachi and M. Barboucha. 'Optimization of a fuzzy classification by evolutionary strategies'. In proceedings of SPIE Conf. , 6 th international conference on quality control by artificial vision. Repulished as an SME Technical Paper by The society of manufacturing Engineers (SME). Paper number MV03-233, IDTP 03 PUB 135, vol. 5132, pp. 220-230 USA, 2003.
  4. Kohei Arai and XianQiang Bu, 'ISODATA clustering with parameter (threshold for merge and split) estimation based on GA: Genetic Algorithm' Reports of the Faculty of Science and Engineering, Saga University, vol. 36, No. 1, pp. 17-23, 2007.
  5. Nargess Memarsadeghi and al. 'A fast implementation of the isodata clustering algorithm', IJCGA, vol. 17, No. 1, pp. 71-103, 2007.
  6. A. EL Allaoui, M. Merzougui, M. Nasri, M. EL Hitmy and H. Ouariachi. 'Optimization of Unsupervised Classification by Evolutionary Strategies'. IJCSNS International Journal of Computer Science and Network Security, ISSN: 1738-7906, vol. 10 No. 6 pp. 325-332 June, 2010.
  7. M. Merzougui, A. EL Allaoui, M. Nasri, M. EL Hitmy and H. Ouariachi. 'Unsupervised classification using evolutionary strategies approach and the Xie and Beni criterion'. IJAST International Journal of Advanced Science and Technology, ISSN: 2005-4238, vol. 19, pp 43-58 June, 2010.
  8. Presberger, T. , Koch, M. 'Comparison of evolutionary strategies and genetic algorithms for optimization of a fuzzy controller'. Proc. of EUFIT'95, Aachen, Germany, august 1995.
  9. Sarkar, M. et al. 'A clustering algorithm using an evolutionary-based approach'. Pattern Recognition Letters, 1997.
  10. Presberger, T. , Koch, M. 'Comparison of evolutionary strategies and genetic algorithms for optimization of a fuzzy controller'. Proc. of EUFIT'95, Aachen, Germany, august 1995.
  11. H. Ouariachi, 'Classification non –Supervisée de données par les réseaux de neurones et par une approche évolutionniste : application à la segmentation d'images'. Thèse de doctorat. Université Mohammed premier Oujda 2001.
  12. Renders, J. M. , 'Algorithmes génétiques et Réseaux de Neurones'. Editions HERMES, 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Classification Segmentation by pixel classification Isodata algorithm evolutionary strategies

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