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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.


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