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Image Binarization of Grey Level Images using Elitist Genetic Algorithm

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 1
Year of Publication: 2012
Amlan Raychaudhuri
Shruti Khandelwal
Sneha Chhalani
Nikhita Kakarania

Amlan Raychaudhuri, Shruti Khandelwal, Sneha Chhalani and Nikhita Kakarania. Article: Image Binarization of Grey Level Images using Elitist Genetic Algorithm. International Journal of Computer Applications 50(1):49-53, July 2012. Full text available. BibTeX

	author = {Amlan Raychaudhuri and Shruti Khandelwal and Sneha Chhalani and Nikhita Kakarania},
	title = {Article: Image Binarization of Grey Level Images using Elitist Genetic Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {1},
	pages = {49-53},
	month = {July},
	note = {Full text available}


Image binarization is a technique of converting a grey level image into a binarized image consisting of only two pixel intensities, i. e. , black and white. Elitist Genetic Algorithm along with K-means clustering technique used here facilitates the gradual partition of the image into either of the two intensities by finding a suitable threshold value for the same. Elitist Genetic Algorithm is an improvised version of Simple GA which preserves the best results for subsequent optimization steps. Genetic Algorithms are imitation of the process of natural selection that aims at keeping the best, discarding the rest. The algorithm stops when a suitably chosen fitness function optimizes the fitness value obtained in every iteration using the operators of GA like selection, crossover, and mutation till no further change in fitness value is noticed. The result is an output image showing the binarized form of the input image.


  • Schwefel, H. P. and Rudolph, G. 1995. Contemporary evolution strategies. Advances in artificial life, 893 – 907.
  • Paulinas, M. and Ušinskas, A. 2007. Survey of genetic algorithms applications for image enhancement and segmentation, Information technology and control, 36(3), 278-284.
  • Mitchell, M. 1996. An introduction to genetic algorithms. The MIT Press, 208.
  • Holland, J. H. 1975. Adaptation in Natural and Artificial Systems, MIT Press.
  • Faber, V. 1994. Clustering and the Continuous k-Means Algorithm. Los Alamos Science, 138-144.
  • Ostu, N. 1978. A thresholding selection method from gray-level histogram, IEEE Trans. Systems Man Cybernet. SMC-8, 62-66.
  • Sahoo, P. K. , Soltani, S. and Wong, A. K. C. 1988. A survey of thresholding technique, Comput. Vision Graphics Image Process. 41, 233-260.
  • Kapur, J. N. , Sahoo, P. K. and Wong, A. K. C. 1985. A new method for gray-level picture thresholding using the entropy of the histogram, Computer Vision Graphics Image Process. 29, 273-285.
  • Lee, S. U. , Chung, S. Y. and Park, R. H. 1990. A comparative performance study of several global thresholding techniques for segmentation, CVGIP 52, 171-190.
  • Boukharouba, S. , Rebordao, J. M. and Wendel, P. L. 1985. An amplitude segmentation method based on the distribution function of an image, Computer Vision Graphics Image Process. 29, 47-59.
  • Wang, S. and Haralick, R. M. 1984. Automatic multithreshold selection, Computer Vision Graphics Image Process. 25, 46-67.
  • Kohler, R. 1981. A segmentation system based on thresholding, Computer Graphics Image Process. 15, 319-338.
  • Papamarkos, N. and Gatos, B. 1994. A new approach for multilevel threshold selection, CVGIP: Graphical Models Image Process. 56(5), 357-370.
  • Niblack, W. 1986. An Introduction to Digital Image Processing. Prentice Hall, Eaglewood Cliffs 115–116.
  • Sauvola, J. and Pietikainen, M. 2000. Adaptive document image binarization. Pattern Recogn. 33(2), 225–236.
  • Shaikh, S. H. , Maiti, A. K. and Chaki, N. 2011. A New Image Binarization Method using Iterative Partitioning, Springer Journal on Machine Vision and Applications; 281-286.
  • Raychaudhuri, A. and Dutta, J. 2012. Image Binarization Using Multi-Layer Perceptron: A Semi-Supervised Approach, International Journal of Engineering Innovation & Research, 1(2), 64-69.