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An Improved Particle Swarm Optimization Method for Color Image Segmentation

V. Sheshathri1 , S. Sukumaran2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-1 , Page no. 118-124, Jan-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i1.118124

Online published on Jan 31, 2019

Copyright © V. Sheshathri, S. Sukumaran . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: V. Sheshathri, S. Sukumaran, “An Improved Particle Swarm Optimization Method for Color Image Segmentation,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.118-124, 2019.

MLA Style Citation: V. Sheshathri, S. Sukumaran "An Improved Particle Swarm Optimization Method for Color Image Segmentation." International Journal of Computer Sciences and Engineering 7.1 (2019): 118-124.

APA Style Citation: V. Sheshathri, S. Sukumaran, (2019). An Improved Particle Swarm Optimization Method for Color Image Segmentation. International Journal of Computer Sciences and Engineering, 7(1), 118-124.

BibTex Style Citation:
@article{Sheshathri_2019,
author = {V. Sheshathri, S. Sukumaran},
title = {An Improved Particle Swarm Optimization Method for Color Image Segmentation},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2019},
volume = {7},
Issue = {1},
month = {1},
year = {2019},
issn = {2347-2693},
pages = {118-124},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3471},
doi = {https://doi.org/10.26438/ijcse/v7i1.118124}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i1.118124}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3471
TI - An Improved Particle Swarm Optimization Method for Color Image Segmentation
T2 - International Journal of Computer Sciences and Engineering
AU - V. Sheshathri, S. Sukumaran
PY - 2019
DA - 2019/01/31
PB - IJCSE, Indore, INDIA
SP - 118-124
IS - 1
VL - 7
SN - 2347-2693
ER -

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Abstract

Color image segmentation can be treated as a process of dividing a color image into some constituent regions. This paper presents color image segmentation method using an Improved Particle Swarm Optimization (IPSO). The RGB color image taken as an input image and remove the noise using Gaussian filter. The obtained preprocessed image the components are separated and find the object regions separately. All the ungrouped pixels would be detected and put in the nearest region. The main purpose of proposed IPSO method is used to find the best values of thresholds, particles and position that can give us an appropriate partition for a target image. This method tries to treat pixels as particles and provide them search space and motivated with IPSO. It findings to better optimized region and produces more accurate segmentation results for color images. The proposed method is tested on different single and group of color images are taken as the input image and the experimental results demonstrate its effectiveness.

Key-Words / Index Term

IPSO, Gaussian Filter, Color Component

References

[1] Akhilesh Chander, Amitava Chatterjee and Patrick Siarry, “A New Social and Momentum Component adaptive PSO Algorithm for Image Segmentation”, Expert Systems with Applications, Elsevier, Vol.38, Issue. 5, pp. 4998-5004, 2011.
[2] Anita Tandan, Rohit Raja and Yamini Chouhan, “Image Segmentation based on Particle Swarm Optimization Technique”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol. 3, Issue. 2, pp.257-260, 2014.
[3] A.Borji, M.Hamidi and A.M.Eftekhari Moghadam, “CLPSO-Based Fuzzy Color Image Segmentation”, IEEE, pp. 508-513, 2007.

[4] E.Boopathi Kumar and V.Thiagarasu, “Color Channel Extraction in RGB Images for Segmentation”, 2nd International Conference on Communication and Electronics Systems(ICCES), IEEE, ISBN: 978-1-5090-5013-0, pp. 234-239, 2017.
[5] Chenxue Wang and Junzo Watada, “Robust Color Image Segmentation by Karhunen-Loeve Transform based Otsu Multi-thresholding and K-means Clustering”, Fifth International Conference on Genetic and Evolutionary Computing, IEEE, pp.377-380, 2011.
[6] Chi-Yu Lee, Jin-Jang Leou and Han-Hui Hsiao, “Saliency-Directed Color Image Segmentation using Modified Particle Swarm Optimization”, Signal Processing, Elsevier, Vol. 92, pp. 1-18, 2012.
[7] Dipak Kumar Kole and Amiya Halder, “An Efficient Dynamic Image Segmentation Algorithm using A Hybrid Technique based on Particle Swarm Optimization and Genetic Algorithm”, International Conference on Advances in Computer Engineering, IEEE, Computer Society, pp. 252-255, 2010.
[8] Firas Ajil Jassim, Fawzi H. Altaani, “Hybridization of Otsu Method and Median Filter for Color Image Segmentation”, International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, Vol. 3, Issue 2, 2013.
[9] Kajal Gautam and Rahul Singhai, “Color Image Segmentation using Particle Swarm Optimization in Lab Color Space”, International Journal of Engineering Development and Research (IJEDR), Vol.6, Issue. 1, pp. 373-377, ISSN: 2321-9939, 2018.
[10] Kiran Ashok Bhandari and Manthalkar Ramanchandra R, “A New Watershed Segmentation (NWS) and Particle Swarm Optimization (PSO-SVM) Techniques in Remote Sensing Image Retrieval”, Proceedings of 3rd International conference on Reliability, Infocom Technologies and Optimization, ISBN: 978-1-4799-6895-4, IEEE, 2014.
[11] H.Li, H.He and Y.Wen, “Dynamic Particle Swarm Optimization and K-Means Clustering Algorithm for Image Segmentation”, International Journal for Light and Electron Optics, Vol.126, Issue 24, pp.4817-4822, 2015.
[12] Manas Yetirajam and Pradeep Kumar Jena, “Enhanced Color Image Segmentation of Foreground Region using Particle Swarm Optimization”, International Journal of Computer Application, Vol. 57, No. 8, pp.18-23, 2012.
[13] Molka Dhieb, Sobeur Masmoudi, Mohamed Ben Messaoud and Faten Ben Afria, “2-D Entropy Image Segmentation on Thresholding Based on Particle Swarm Optimization”, 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP’2014), pp.143-147, March 17-19, 2014.
[14] D.Napoleon, A.Shameena and R.Santhoshi, “Color Image Segmentation using OTSU Method and Color Space”, IJCA Proceedings on International Conference on Innovation in Communication, Information and Computing (ICICIC), No. 1, 2013.
[15] Parag Puranik, Preeti Bajaj, Ajith Abraham, Prasanna Palsodkar and Amol Deshmukh, “Human Perception-Based Color Image Segmentation using Comprehensive Learning Particle Swarm Optimization”, 2nd International Conference on Emerging Trends in Engineering & Technology, IEEE, ISBN: 978-1-4244-5250-7, 2009.
[16] Saeed Mirghasemi, Ramesh Rayudu and Mengjie Zhang, “A New Image Segmentation Algorithm Based on Modified Seeded Region Growing and Particle Swarm Optimization”, 28th International Conference on Image and Vision Computing, IEEE, pp. 382-387, 2013.
[17] M. Sezgin, B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation", J. Electron. Imaging, Vol. 13, No. 1, pp. 146-165, 2004.

[18] Shima Afzali, Bing Xue, Harith Al-Sahaf and Mengjie Zhang, “A Supervised Feature Weighting Method for Salient Object Detection using Particle Swarm Optimization”, IEEE Symposium Series on Computational Intelligence (SSCI), ISBN: 978-1-5386-2727-3, 2017.
[19] A. A. Younes, I. Truck, and H. Akdaj, "Color Image Profiling Using Fuzzy Sets," Turk J Elec. Engin., Vol.13, No.3, 2005.
[20] Zhang Xue-Xi and Yang Yi-Min, “Hybrid Intelligent Algorithms for Color Image Segmentation”, Chinese Control and Decision Conference(CCDC), IEEE, pp.264-268, 2008.