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Image Binarization of Grey Level Images using Elitist Genetic Algorithm

by Amlan Raychaudhuri, Shruti Khandelwal, Sneha Chhalani, Nikhita Kakarania
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 50 - Number 1
Year of Publication: 2012
Authors: Amlan Raychaudhuri, Shruti Khandelwal, Sneha Chhalani, Nikhita Kakarania
10.5120/7739-0791

Amlan Raychaudhuri, Shruti Khandelwal, Sneha Chhalani, Nikhita Kakarania . Image Binarization of Grey Level Images using Elitist Genetic Algorithm. International Journal of Computer Applications. 50, 1 ( July 2012), 49-53. DOI=10.5120/7739-0791

@article{ 10.5120/7739-0791,
author = { Amlan Raychaudhuri, Shruti Khandelwal, Sneha Chhalani, Nikhita Kakarania },
title = { Image Binarization of Grey Level Images using Elitist Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { July 2012 },
volume = { 50 },
number = { 1 },
month = { July },
year = { 2012 },
issn = { 0975-8887 },
pages = { 49-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume50/number1/7739-0791/ },
doi = { 10.5120/7739-0791 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:47:13.192042+05:30
%A Amlan Raychaudhuri
%A Shruti Khandelwal
%A Sneha Chhalani
%A Nikhita Kakarania
%T Image Binarization of Grey Level Images using Elitist Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 50
%N 1
%P 49-53
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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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Index Terms

Computer Science
Information Sciences

Keywords

Image binarization Genetic Algorithm K-means Clustering Image thresholding Elitism

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