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Bio-Inspired Algorithms for Color Image Segmentation

by Salima Nebti
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 18
Year of Publication: 2013
Authors: Salima Nebti
10.5120/12840-9810

Salima Nebti . Bio-Inspired Algorithms for Color Image Segmentation. International Journal of Computer Applications. 73, 18 ( July 2013), 11-16. DOI=10.5120/12840-9810

@article{ 10.5120/12840-9810,
author = { Salima Nebti },
title = { Bio-Inspired Algorithms for Color Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 18 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 11-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number18/12840-9810/ },
doi = { 10.5120/12840-9810 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:26.735138+05:30
%A Salima Nebti
%T Bio-Inspired Algorithms for Color Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 18
%P 11-16
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Effective image segmentation remains a challenging process as it constitutes a critical step to higher level image processing applications such as pattern recognition. In this paper,we present bio-inspired formulationto perform unsupervised image segmentation. Specifically,we used the Quantum PSO, the hybrid Gravitational PSO algorithm, a cooperative gravitational approach and the bees approach as powerful global classifiers to optimize the partition of image data into homogenous regions. The segmentation accuracy based on the bees' algorithm has the highest accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Image segmentation Quantum PSO the Gravitational search algorithm cooperative coevolution the bees algorithm

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