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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2018

23-04-2015 | Original Article

Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms

Authors: Sushil Kumar, Millie Pant, Manoj Kumar, Aditya Dutt

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2018

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Abstract

Due to the complexity of underlying data in a color image, retrieval of specific object features and relevant information becomes a complex task. Colour images have different color components and a variety of colour intensity which makes segmentation very challenging. In this paper we suggest a fitness function based on pixel-by-pixel values and optimize these values through evolutionary algorithms like differential evolution (DE), particle swarm optimization (PSO) and genetic algorithms (GA). The corresponding variants are termed GA-SA, PSO-SA and DE-SA; where SA stands for Segmentation Algorithm. Experimental results show that DE performed better in comparison of PSO and GA on the basis of computational time and quality of segmented image.
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Metadata
Title
Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms
Authors
Sushil Kumar
Millie Pant
Manoj Kumar
Aditya Dutt
Publication date
23-04-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0360-7

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