research-article

High-dimensional multi-level image thresholding using self-organizing migrating algorithm

Authors:

Seyed Jalaleddin Mousavirad,

Gerald Schaefer,

Iakov KorovinAuthors Info & Claims

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Pages 1454 - 1459

https://doi.org/10.1145/3377929.3398125

Published: 08 July 2020 Publication History

Abstract

Multi-level image thresholding is a common approach to image segmentation for which population-based metaheuristic algorithms present an interesting alternative to conventional methods that are based on a exhaustive search. In this paper, we propose a novel multi-level image thresholding algorithm based on the Self-Organizing Migrating Algorithm (SOMA), in particular SOMA Team To Team Adaptive (SOMA T3A), a recent variant of SOMA, and an entropy-based fitness function. We evaluate our algorithm on a set of benchmark images on high-dimensional search spaces and with regards to fitness function value and peak signal-to-noise ratio (PSNR). Experimental results demonstrate excellent thresholding performance and our algorithm to outperform nine other state-of-the-art metaheuristics.

References

[1]

K. Charansiriphaisan, S. Chiewchanwattana, and K. Sunat. 2014. A global multi-level thresholding using differential evolution approach. Mathematical Problems in Engineering 2014 (2014).

[2]

E. Cuevas, H. Sossa, et al. 2013. A comparison of nature inspired algorithms for multi-threshold image segmentation. Expert Systems with Applications 40, 4 (2013), 1213--1219.

Digital Library

[3]

D. Davendra and I. Zelinka (Eds.). 2016. Self-Organizing Migrating Algorithm - Methodology and Implementation. Springer.

[4]

J. Derrac, S. García, D. Molina, and F. Herrera. 2011. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1, 1 (2011), 3--18.

[5]

Q. B. Diep. 2019. Self-organizing migrating algorithm Team To Team adaptive - SOMA T3A. In IEEE Congress on Evolutionary Computation. 1182--1187.

[6]

S. Gupta and K. Deep. 2019. Improved sine cosine algorithm with crossover scheme for global optimization. Knowledge-Based Systems 165 (2019), 374--406.

[7]

D. Martin, C. Fowlkes, D. Tal, and J. Malik. 2001. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. In 8th International Conference on Computer Vision, Vol. 2. 416--423.

[8]

S. Mirjalili. 2015. The ant lion optimizer. Advances in Engineering Software 83 (2015), 80--98.

Digital Library

[9]

S. Mirjalili. 2016. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems 96 (2016), 120--133.

Digital Library

[10]

S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili. 2017. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software 114 (2017), 163--191.

Digital Library

[11]

S. Mirjalili, S. M. Mirjalili, and A. Hatamlou. 2016. Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications 27, 2 (2016), 495--513.

Digital Library

[12]

S. J. Mousavirad and H. Ebrahimpour-Komleh. 2015. Entropy based optimal multilevel thresholding using cuckoo optimization algorithm. In 11th International Conference on Innovations in Information Technology. IEEE, 302--307.

[13]

S. J. Mousavirad and H. Ebrahimpour-Komleh. 2016. Optimal multilevel image thresholding using the teaching-learning-based optimization. Machine Vision and Image Processing 2, 2 (2016), 51--62.

[14]

S. J. Mousavirad and H. Ebrahimpour-Komleh. 2017. Human mental search: a new population-based metaheuristic optimization algorithm. Applied Intelligence 47, 3 (2017), 850--887.

Digital Library

[15]

S. J. Mousavirad and H. Ebrahimpour-Komleh. 2017. Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms. Evolutionary Intelligence 10, 1-2 (2017), 45--75.

[16]

S. J. Mousavirad and H. Ebrahimpour-Komleh. 2019. Human mental search-based multilevel thresholding for image segmentation. Applied Soft Computing (2019).

[17]

S. J. Mousavirad, G. Schaefer, and H. Ebrahimpour-Komleh. 2019. A Benchmark of Population-Based Metaheuristic Algorithms for High-Dimensional Multi-Level Image Thresholding. In IEEE Congress on Evolutionary Computation. 2394--2401.

[18]

K. Price, N. Awad, M. Ali, and P. Suganthan. 2018. Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. Technical Report. Nanyang Technological University.

[19]

N. Raja, V. Rajinikanth, and K. Latha. 2014. Otsu based optimal multilevel image thresholding using firefly algorithm. Modelling and Simulation in Engineering 2014 (2014), 37.

Digital Library

[20]

Y. Shi and R. Eberhart. 1998. A modified particle swarm optimizer. In IEEE International Conference on Evolutionary Computation. 69--73.

[21]

D. Simon. 2008. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation 12, 6 (2008), 702--713.

Digital Library

[22]

R. Storn and K. Price. 1997. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 4 (1997), 341--359.

Digital Library

[23]

S. Wang, H. Jia, and X. Peng. 2019. Modified salp swarm algorithm based multilevel thresholding for color image segmentation. Mathematical Biosciences and Engineering 17, 1 (2019), 700--724.

[24]

D. Whitley. 1994. A genetic algorithm tutorial. Statistics and Computing 4, 2 (1994), 65--85.

[25]

X.-S. Yang. 2010. Firefly algorithm, stochastic test functions and design optimisation. arXiv preprint arXiv:1003.1409 (2010).

[26]

X.-S. Yang and S. Deb. 2009. Cuckoo search via Lévy flights. In World Congress on Nature & Biologically Inspired Computing. 210--214.

[27]

P.-Y. Yin. 2007. Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184, 2 (2007), 503--513.

[28]

I. Zelinka and J. Lampinen. 2000. SOMA - self-organizing migrating algorithm. In 6th International Conference on Soft Computing.

Cited By

Mousavirad SOliva DChakrabortty RZabihzadeh DHinojosa S(2022)Population-based self-adaptive Generalised Masi Entropy for image segmentationKnowledge-Based Systems10.1016/j.knosys.2022.108610245:COnline publication date: 7-Jun-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108610
Skanderova L(2022)Self-organizing migrating algorithm: review, improvements and comparisonArtificial Intelligence Review10.1007/s10462-022-10167-856:1(101-172)Online publication date: 4-Apr-2022
https://doi.org/10.1007/s10462-022-10167-8
Mousavirad SZabihzadeh DOliva DPerez-Cisneros MSchaefer G(2021)A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image SegmentationEntropy10.3390/e2401000824:1(8)Online publication date: 21-Dec-2021
https://doi.org/10.3390/e24010008

Index Terms

High-dimensional multi-level image thresholding using self-organizing migrating algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
  2. Computer graphics
    1. Image manipulation
      1. Image processing
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization
      2. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

Recommendations

Effective image clustering using self-organizing migrating algorithm
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Image segmentation is an important task in computer vision. Clustering is a common image segmentation approach which divides an image into homogeneous regions, but conventional clustering algorithms such as k-means have a tendency of getting stuck in ...
Colour quantisation using self-organizing migrating algorithm
GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Selecting these colour, which comprise a colour palette, is a challenging task since they determine the resulting image quality. In this ...
A Benchmark of Population-Based Metaheuristic Algorithms for High-Dimensional Multi-Level Image Thresholding
2019 IEEE Congress on Evolutionary Computation (CEC)
Multi-level image thresholding is a popular approach for image segmentation where the image is divided into several non-overlapping regions based on the image histogram. Conventional algorithms for multi-level image thresholding are time-consuming. This ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

July 2020

1982 pages

ISBN:9781450371278

DOI:10.1145/3377929

General Chair:
Carlos Artemio Coello Coello
CINVESTAV-IPN

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Russian Foundation for Basic Research

Conference

GECCO '20

Sponsor:

SIGEVO

GECCO '20: Genetic and Evolutionary Computation Conference

July 8 - 12, 2020

Cancún, Mexico

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
44
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mousavirad SOliva DChakrabortty RZabihzadeh DHinojosa S(2022)Population-based self-adaptive Generalised Masi Entropy for image segmentationKnowledge-Based Systems10.1016/j.knosys.2022.108610245:COnline publication date: 7-Jun-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.108610
Skanderova L(2022)Self-organizing migrating algorithm: review, improvements and comparisonArtificial Intelligence Review10.1007/s10462-022-10167-856:1(101-172)Online publication date: 4-Apr-2022
https://doi.org/10.1007/s10462-022-10167-8
Mousavirad SZabihzadeh DOliva DPerez-Cisneros MSchaefer G(2021)A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based Multi-Level Image SegmentationEntropy10.3390/e2401000824:1(8)Online publication date: 21-Dec-2021
https://doi.org/10.3390/e24010008

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents