CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Reboost Image Segmentation using Genetic Algorithm

by Ashutosh Jaiswal, Lavika Kurda, Vijai Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 19
Year of Publication: 2013
Authors: Ashutosh Jaiswal, Lavika Kurda, Vijai Singh
10.5120/12076-7656

Ashutosh Jaiswal, Lavika Kurda, Vijai Singh . Reboost Image Segmentation using Genetic Algorithm. International Journal of Computer Applications. 69, 19 ( May 2013), 1-7. DOI=10.5120/12076-7656

@article{ 10.5120/12076-7656,
author = { Ashutosh Jaiswal, Lavika Kurda, Vijai Singh },
title = { Reboost Image Segmentation using Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 19 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number19/12076-7656/ },
doi = { 10.5120/12076-7656 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:31:39.582079+05:30
%A Ashutosh Jaiswal
%A Lavika Kurda
%A Vijai Singh
%T Reboost Image Segmentation using Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 19
%P 1-7
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper present a Improved Algorithm for Image Segmentation System for a RGB colour image, and presents a proposed efficient colour image segmentation algorithm based on evolutionary approach i. e. improved Genetic algorithm. The proposed technique, without any predefined parameters determines the optimum number of clusters for colour images. The optimal number of clusters is obtained by using maximum fitness value of population selection. The advantage of this method lies in the fact that no prior knowledge related to number of clusters is required to segment the color image. Proposed algorithm strongly supports the better quality of segmentation. Experiments on standard images have given the satisfactory and comparable results with other techniques.

References
  1. W. Frei and C. Chen, "Fast Boundary Detection: A Generalization and New Algorithm," IEEE Trans. Computers, vol. C-26, no. 10, pp. 988-998, Oct. 1977.
  2. J. Canny, "A computational approach to edge detection," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, Nov. 1986.
  3. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper Saddle River, NJ: Prentice-Hall, 2001, pp. 572-585.
  4. W. K. Pratt, Digital Image Processing. New York, NY: Wiley-Interscience, 1991, pp. 491-556.
  5. Suryakant, Neetu Kushwaha, "Edge Detection using Fuzzy Logic in Matlab," ISSN: 2277 128X, Volume 2, Issue 4, April 2012.
  6. Gonzalez and Woods, "Digital image processing", 2nd Edition, prentice hall, 2002.
  7. Kenneth R. Castelman, "Digital image processing", Tsinghua Univ Press, 2003.
  8. Du Gen-yuan,Miao Fang,Tian Sheng-li,Guo Xi-rong. ,"Remote Sensing Image Sequence Segmentation Based on the Modified Fuzzy C-means", Journal of Software, Vol. 5, No. 1, PP. 28-35, 2009.
  9. N. Senthilkumaran and R. Rajesh, "Edge Detection Techniques for Image Segmentation - A Survey", Proceedings of the International Conference on Managing Next Generation Software Applications (MNGSA-08), 2008, pp. 749-760.
  10. Sharmishtha Mitra, Amit Mitra and Debasis Kundu, "Genetic algorithm and M-estimator based robust sequential estimation of parameters of nonlinear sinusoidal signals", Commun Nonlinear Sci Numer Simulat, October 2010.
  11. Jiun-Hung Chen, Chu-Song Chen, and Yong-Sheng Chen, "Fast Algorithm for Robust Template Matching With M-Estimators", IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 51, NO. 1, JANUARY 2003.
  12. Jun Zhang, Kitakyushu Jinglu Hu. "Image Segmentation Based on 2D Otsu Method with Histogram Analysis" Computer Science and Software Engineering, 2008 International Conference
  13. Leo Grady and Eric L. Schwartz "Isoperimetric Graph Partitioning for Image Segmentation"Pattern Analysis and Machine Intelligence, IEEE Transactions.
  14. Pateek Gupta, Sargam Saxena, Sonali Singh, Saumya Dhami and Vijai Singh, "Color Image Segmentation: A State of the Art Survey", International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 8, Number 1 (2012), pp. 17-25 © Research India Publications.
  15. Utkarsh Kumar Shah and Prof. A. Mukherjee, "BTP REPORT Image segmentation by region growing", April 15, 2010.
  16. Shilpa Kamdi1, R. K. Krishna2,1,2-Rajiv Gandhi College of Engineering,Research and Technology, "Image Segmentation and Region Growing Algorithm" , feb 1, 2012,
  17. S. Beucher, F. Meyer,"The morphological approach to segmentation: The watershed transform", in Mathematical Morphology Image Processing, E. R. Dougherty, Ed. New York Marcel Dekker, 1993, vol. 12, pp. 433–481.
  18. A. N. Moga, M. Gabbouj, "Parallel image component labeling with watershed transformation", IEEE Trans. Pattern Anal. MachineIntell. vol. 19, pp. 441–450. J. M Gauch, "Image segmentation and analysis via multi-scale gradient watershed hierarchies", IEEE Trans Image Processing, vol. 8, pp. 69–79, 2000
  19. O. F. Olsen, M. Nielsen, "Multi-scale gradient magnitude watershed segmentation", in ICIAP' 97–9th Int Conference on Image Analysis and Processing, ser. Lecture Notesin Computer Science. Berlin, Germany: Springer-Verlag, 2001, vol. 1310, pp. 6–13.
  20. JL. Vincent, "Morphological gray scale reconstruction in image analysis: Applications and efficient algorithms", IEEE Trans. Image Processing, vol. 2, 1993.
  21. Mandeep Kaur, Gagandeep Jindal, "Medical Image Segmentation using Marker Controlled Watershed Transformation", IJCST Vol. 2, Issue 4, Oct . - Dec. 2011.
  22. Faguo Yang 3, Tianzi Jiang and Yong Fan , "A Parallel Genetic Algorithm for Cell Image Segmentation" 1;2, 4 National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing 100080, P. R. China David J. Evans 5 Department of Computing, Nottingham Trent University Nottingham, NG1 4BU, UK.
  23. L. Tang, L. Tian, B. L. Steward , "COLOR IMAGE SEGMENTATION WITH GENETIC ALGORITHM FOR IN-FIELD WEED SENSING".
  24. S. Chabrier, C. Rosenberger, B. Emile Laboratoire Terre-Oc´ean Universit´e de la Polyn´esie francaise B. P. 6570 98702 FAA'A, Tahiti - Polyn´esie Franaise, Laboratoire GREYC ENSICAEN - Universit´e de Caen – CNRS 6 Boulevard Mar´echal Juin, 14000 Caen cedex, France EURASIP journal on Video and Image processing (2008) 1-23.
  25. Thamilselvan Rakkiannan and Balasubramanie Palanisamy, "Hybridization of Genetic Algorithm with Parallel Implementation of Simulated Annealing for Job Shop Scheduling" American Journal of Applied Sciences 9 (10): 1694-1705, 2012.
  26. Kikuo Fujita, Shinsuke Akagi and Noriyasu Hirokawa, "Hybrid Approach for Optimal Nesting Using a Genetic Algorithm and a Local Minimization Algorithm" Osaka University Suita, Osaka, JAPAN.
  27. M. Srinivas, and L. M. Patnaik, Fellow, "Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms" IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS, VOL. 24, NO. 4, APRIL 1994.
  28. Mohamad Awad, Kacem Chehdi, and Ahmad Nasri , "Multicomponent Image Segmentation Using a Genetic Algorithm and Artificial Neural Network", IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 4, NO. 4, OCTOBER 2007.
  29. Falkenauer, E. , "Genetic Algorithms and Grouping Problems", John Wiley & Sons, Boston, 1998.
  30. Peng-Yeng Yin, "A fast scheme for optimal thresholding using genetic algorithms" Signal Processing, (72):85–95, 1999.
  31. Minglun Gong and Yee-Hong Yang, "Genetic-based multiresolution color image segmentation" Vision Interface, pages 141–148, 2001.
  32. D. N. Chun and H. S. Yang, "Robust image segmentation using genetic algorithm with a fuzzy measure" Pattern Recognition, 7(29):1195–1211, 1996.
  33. B. Bhanu and S. Lee, "Genetic Learning for Adaptive Image Segmentation" Kluwer Academic Press, 1994.
  34. Y. Delignon A. Marzouki and W. Pieczynski, "Estimation of generalized mixtures and its application in image segmentation" IEEE Transactions on Image Processing, 6(10):1364–1375, October 1997.
  35. Zhenyu Wu and Richard Leahy, "An Optimal Graph theoretic approach to data clustering: Theory and its Application to Image Segmentation", IEEE Transactions on pattern analysis and machine intelligence, Vol. 15 No. 11, Nov. 1993.
  36. Amiya Halder and Nilavra Pathak, "An Evolutionary Dynamic Clustering Based Colour Image Segmentation", International Journal of Image Processing (IJIP), Volume (4): Issue (6).
  37. P. Scheunders, "A GENETIC C-MEANS CLUSTERING ALGORITHM APPLIED TO COLOR IMAGE QUANTIZATION", Vision Lab, Dept. of Physics, RUCA University of Antwerp, Groenenborgerlaan 171, 2020 Antwerpen, Belgium.
  38. Li Zhuo, Jing Zheng, Fang Wang, Xia Li, Bin Ai and Junping Qian, "A GENETIC ALGORITHM BASED WRAPPER FEATURE SELECTION METHOD FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES USING SUPPORT VECTOR MACHINE", School of Geographical sciences, Guangzhou University, Guangzhou 510006, China) Commission VII, WG VII/3.
  39. S. Cagnoni, A. B. Dobrezeneicki, R. Pauli, J. C. Yanch, "Genetic algorithm based interactive segmentation of 3D medical images".
Index Terms

Computer Science
Information Sciences

Keywords

Color image segmentation Genetic algorithm Clustering

Powered by PhDFocusTM