CFP last date
20 December 2024
Reseach Article

Color Image Segmentation using CIELab Color Space using Ant Colony Optimization

by Seema Bansal, Deepak Aggarwal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 9
Year of Publication: 2011
Authors: Seema Bansal, Deepak Aggarwal
10.5120/3590-4978

Seema Bansal, Deepak Aggarwal . Color Image Segmentation using CIELab Color Space using Ant Colony Optimization. International Journal of Computer Applications. 29, 9 ( September 2011), 28-34. DOI=10.5120/3590-4978

@article{ 10.5120/3590-4978,
author = { Seema Bansal, Deepak Aggarwal },
title = { Color Image Segmentation using CIELab Color Space using Ant Colony Optimization },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 9 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 28-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number9/3590-4978/ },
doi = { 10.5120/3590-4978 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:22.338433+05:30
%A Seema Bansal
%A Deepak Aggarwal
%T Color Image Segmentation using CIELab Color Space using Ant Colony Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 9
%P 28-34
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation plays vital role to understand an image. Only proper understanding of an image tells that what it represents and the various objects present in the image. In this paper we have proposed a new approach by using CIELab color space and Ant based clustering for the segmentation of color images. Image segmentation process divides an image into distinct regions with property that each region is characterized by unique feature such as intensity, color etc. This paper elaborates the ant based clustering for image segmentation. CMC distance is used to calculate the distance between pixels as this color metric gives good results with CIELab color space. Results shows the segmentation performed using ant based clustering and also shows that number of clusters for the image with particular CMC distance also varies. In order to evaluate the performance of proposed technique, MSE (Mean Square Error) is used. MSE is the global quality measure based on pixel difference. To verify our work, we have compared the results with results of color image quantization using LAB color model based on Bacteria Foraging Optimization [13].

References
  1. Anil Z Chitade et. al., “COLOUR BASED IMAGE SEGMENTATION USING K-MEANS CLUSTERING”, International Journal of Engineering Science and Technology Vol. 2(10), 2010, 5319-5325.
  2. Anna Veronica Baterina et al., “Image Edge Detection Using Ant Colony Optimization”, INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING, Issue 2, Volume 4, 2010.
  3. C. Immaculate Mary et al., “A Modified Ant-based Clustering for Medical Data”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2253-2257.
  4. Christine Connolly et al., “A Study of Efficiency and Accuracy in the Transformation from RGB to CIELAB Color Space”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 6, NO. 7, JULY 1997.
  5. Ganesan P et al., “Segmentation and Edge Detection of Color Images Using CIELAB Color Space and Edge Detectors”, 978-1-4244-9005-9/10/$26.00 ©2010 IEEE.
  6. Hsin-Chia Chen et al., “Contrast-Based Color Image Segmentation”, IEEE SIGNAL PROCESSING LETTERS, VOL. 11, NO. 7, JULY 2004.
  7. Liqiang Liu and HaijiaoRen et al., “Ant Colony Optimization Algorithm Based on Space Contraction”, Proceedings of the 2010 IEEE, International Conference on Information and Automation, June 20 - 23, Harbin, China.
  8. Myung-Eun Lee1 et al., “Segmentation of Brain MR Images using an Ant Colony Optimization Algorithm”, 978-0-7695-3656-9/09 $25.00 © 2009 IEEE.
  9. N.R. Pal et al., “A review on image segmentation techniques”, Pattern Recognition 9(26): 1277–1294, 1993.
  10. O.A. Mohamed Jafar, “Ant-based Clustering Algorithms: A Brief Survey “, International Journal of Computer Theory and Engineering, Vol. 2, No. 5, October, 2010, 1793-8201.
  11. R. Laptik et al., “Application of Ant Colony Optimization for Image Segmentation”, ISSN 1392 – 1215, 2007. No. 8 (80).
  12. Salima Ouadfel et al., “An Efficient Ant Algorithm for Swarm-Based Image Clustering”, Journal of Computer Science 3 (3): 162-167, 2007 ISSN 1549-3636 © 2007 Science Publications.
  13. Rajinder Kaur et al., “Color Image Quantization based on Bacteria Foraging Optimization”, International Journal of Computer Applications (0975 – 8887) Volume 25– No.7, July 2011.
  14. PunamThakare et al., “A Study of Image Segmentation and Edge Detection Techniques”, International Journal on Computer Science and Engineering (IJCSE), ISSN : 975-3397 Vol. 3 No. 2 Feb 2011.
  15. Surbhi Gupta et al., “Implementing Color Image Segmentation Using Biogeography Based Optimization”, 2011 International Conference on Software and Computer Applications IPCSIT vol.9 (2011) © (2011) IACSIT Press, Singapore.
  16. S. Thilagamani et al., “A Survey on Image Segmentation Through Clustering”, International Journal of Research and Reviews in Information Sciences ,Vol. 1, No. 1, March 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Ant Clust CMC distance CIELab color space segmentation