|
| An Evolutionary Dynamic Clustering based Colour Image Segmentation
|
|
Full
text: |
 PDF(812.8KB) |
|
|
Source |
International Journal of Image Processing (IJIP) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(11.17MB) |
|
Volume: 4 Issue: 6 |
| |
Pages: 518-676 |
|
Publication
Date: January / February |
|
ISSN
(Online): 1985-2304 |
|
|
|
|
|
Pages |
549 - 556 |
|
Author(s) |
|
|
|
Published
Date |
08-02-2011 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Segmentation, Clustering, Genetic Algorithm, Clustering Metric, Validity Index |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Directory of Open Access Journals (DOAJ) |
| 2. Scribd |
| 3. refSeek |
| 4. Socol@r |
| 5. Docstoc |
| 6. Google Scholar |
| 7. iSEEK |
| 8. WorldCat |
| |
|
| |
|
|
| We have presented a novel Dynamic Colour Image Segmentation (DCIS) System for colour image. In this paper, we have proposed an efficient colour image segmentation algorithm based on evolutionary approach i.e. dynamic GA based clustering (GADCIS). The proposed technique automatically determines the optimum number of clusters for colour images. The optimal number of clusters is obtained by using cluster validity criterion with the help of Gaussian distribution. The advantage of this method is that no a priori knowledge is required to segment the color image. The proposed algorithm is evaluated on well known natural images and its performance is compared to other clustering techniques. Experimental results show the performance of the proposed algorithm producing comparable segmentation results.
|
| |
|
| |
|
| |
| 1 |
Ujjwal Maulik, Sanghamitra Bandyopadhyay, “Genetic algorithm-based clustering technique”, Elsevier Science Ltd., 1999.
|
|
|
| 2 |
Qin Ding and Jim Gasvoda, “A Genetic Algorithm for Clustering on Image Data” in International Journal of Computational Intelligence Vol-1 No-1, 2004.
|
|
|
| 3 |
Hwei-Jen Lin, Fu-Wen Yang and Yang-Ta Kao, “An Efficient GA-based Clustering Technique”, in Tamkang Journal of Science and Engineering Vol-8 No-2, 2005.
|
|
|
| 4 |
Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Pearson Education, 2002.
|
|
|
| 5 |
R. C. Dubes, A. K. Jain, “Clustering techniques: the user’s dilemma”, Pattern Recognition, 1976.
|
|
|
| 6 |
M. Srinivas, Lalit M. Patnaik, “Genetic Algorithms: A Survey”.
|
|
|
| 7 |
D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.
|
|
|
| 8 |
R. H. Turi, “Clustering-Based Color Image Segmentation”, PhD Thesis, Monash University, Australia, 2001.
|
|
|
| 9 |
Mahamed G. H. Omran, Andries P Engelbrecht and Ayed Salman, “Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification”, PWASET Volume 9, 2005.
|
|
|
| 10 |
DW van der Merwe, AP Engelbrecht, “Data Clustering using Particle Swarm Optimization”.
|
|
|
| 11 |
G Ball, D Hall, “A Clustering Technique for Summarizing Multivariate Data”, Behavioral Science, Vol. 12, 1967.
|
|
|
| 12 |
LV Fausett, “Fundamentals of Neural Networks”, Prentice Hall, 1994.
|
|
|
| 13 |
E Forgy, “Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification”, Biometrics, Vol. 21, 1965.
|
|
|
| 14 |
JA Hartigan, Clustering Algorithms, John Wiley & Sons, New York, 1975.
|
|
|
| 15 |
T Kohonen, “Self-Organizing Maps”, Springer Series in Information Sciences, Vol 30, Springer-Verlag, 1995.
|
|
|
| 16 |
S.Z. Selim, M.A. Ismail, K-means type algorithms: a generalized convergence theorem and characterization of local optimality, IEEE Trans. Pattern Anal. Mach. Intell.6 (1984) 81-87.
|
|
|
| 17 |
Dipak Kumar Kole and Amiya Halder ,“ An Efficient Image Segmentation Algorithm using Dynamic GA based Clustering”, International Journal of Logistics and Supply Chain Management , Vol. 2, No. 1,pp.17-20, 2010.
|
|
|
| 18 |
Mofakharul Islam, John Yearwood and Peter Vamplew “Unsupervised Color Textured Image Segmentation Using Cluster Ensembles and MRF Model”, Advances in Computer and Information Sciences and Engineering, 323–328. © Springer Science+Business Media B.V. 2008.
|
|
|
| 19 |
C.S. Wallace, and D..L. Dow, “MML clustering of multi-state, poisson, von mises circular and gaussian distribution”, Statistics and Computing,Vol.10(1), Jan. 2000, pp.73-83.
|
|
|
| 20 |
R. Siddheswar and R.H. Turi, “Determination of Number of Clusters in k-means Clustering and application in Color Image Segmentation”, In Proceedings of the 4th Intl. Conf. on Advances in Pattern Recognition and Digital Techniques (ICAPRDT’99), vol. Calcutta, India, 1999 pages: 137-143.
|
|
|
| 21 |
Wu Yiming, Yang Xiangyu, and Chan Kap Luk, “Unsupervised Color Image Segmentation based on Gaussian Mixture Model”, In Proceedings of the 2003 Joint Conf. of the 4th Intl. Conf. on Information, Communications and Signal Processing, Vol. 1(15-18 Dec. 2003), pages: 541-544.
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| Amiya Halder : Colleagues
|
|
| Nilvra Pathak : Colleagues
|
|
