research-article

HCS-BBD: an effective population-based approach for multi-level thresholding

Authors:

Seyed Jalaleddin Mousavirad,

Gerald Schaefer,

Salvador HinojosaAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1923 - 1930

https://doi.org/10.1145/3449726.3463149

Published: 08 July 2021 Publication History

Abstract

Thresholding is one of the most common techniques for image segmentation where an image is partitioned into several parts based on its histogram of pixel intensities. Conventional algorithms work efficiently for bi-level thresholding where an image is divided into fore- and background, but their efficiency drastically declines for the more complex case of multi-level thresholding due to the exhaustive search that is employed. To address this problem, in this paper we consider multi-level thresholding as an optimisation problem and propose a novel population-based algorithm, HCS-BBD, which is based on cuckoo search (CS) and biogeography-based optimisation (BBO). To this end, HCS-BBD integrates a heterogeneous cuckoo search strategy with a biogeography-based discovery operator. Our findings in comparison to state-of-the-art and recent population-based algorithms on different images convincingly demonstrate HCS-BBD's excellent capability in finding optimal threshold values.

References

[1]

M. Ali, C. W. Ahn, and M. Pant. 2014. Multi-level image thresholding by synergetic differential evolution. Applied Soft Computing 17 (2014), 1--11.

Digital Library

[2]

X. Chen and K. Yu. 2019. Hybridizing cuckoo search algorithm with biogeography-based optimization for estimating photovoltaic model parameters. Solar Energy 180 (2019), 192--206.

[3]

N. J. Cheung, X.-M. Ding, and H.-B. Shen. 2016. A nonhomogeneous cuckoo search algorithm based on quantum mechanism for real parameter optimization. IEEE Transactions on Cybernetics 47, 2 (2016), 391--402.

[4]

M.-A. Díaz-Cortés, N. Ortega-Sánchez, S. Hinojosa, D. Oliva, E. Cuevas, R. Rojas, and A. Demin. 2018. A multi-level thresholding method for breast thermograms analysis using dragonfly algorithm. Infrared Physics & Technology 93 (2018), 346--361.

[5]

X. Ding, Z. Xu, N. J. Cheung, and X. Liu. 2015. Parameter estimation of Takagi-Sugeno fuzzy system using heterogeneous cuckoo search algorithm. Neurocomputing 151 (2015), 1332--1342.

[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]

H. Jia, X. Peng, W. Song, C. Lang, Z. Xing, and K. Sun. 2019. Multiverse optimization algorithm based on Lévy flight improvement for multithreshold color image segmentation. IEEE Access 7 (2019), 32805--32844.

[8]

J. Kennedy and R. Eberhart. 1995. Particle swarm optimization (PSO). In IEEE International Conference on Neural Networks. 1942--1948.

[9]

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.

[10]

S. Mirjalili. 2016. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications 27, 4 (2016), 1053--1073.

Digital Library

[11]

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

Digital Library

[12]

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

[13]

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

[14]

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. 302--307.

[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, S. Rahnamayan, and G. Schaefer. 2020. Many-level image thresholding using a center-based differential evolution algorithm. In Congress on Evolutionary Computation.

[18]

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.

[19]

S. J. Mousavirad, G. Schaefer, and I. Korovin. 2020. High-dimensional multi-level image thresholding using self-organizing migrating algorithm. In Genetic and Evolutionary Computation Conference Companion. 1454--1459.

[20]

S. J. Mousavirad, G. Schaefer, Z. Movahedi, and I. Korovin. 2020. High-dimensional multi-level maximum variance threshold selection for image segmentation: a benchmark of recent population-based metaheuristic algorithms. In Genetic and Evolutionary Computation Conference Companion. 1608--1613.

[21]

N. Otsu. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 1 (1979), 62--66.

[22]

S. Pare, A. Kumar, V. Bajaj, and G. K. Singh. 2017. An efficient method for multilevel color image thresholding using cuckoo search algorithm based on minimum cross entropy. Applied Soft Computing 61 (2017), 570--592.

[23]

E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi. 2009. GSA: a gravitational search algorithm. Information Sciences 179, 13 (2009), 2232--2248.

Digital Library

[24]

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

[25]

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

Digital Library

[26]

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

[27]

D. Wolpert and G. Macready. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 1 (1997), 67--82.

Digital Library

[28]

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

[29]

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

[30]

L. Zhang, L. Zhang, X. Mou, and D. Zhang. 2011. FSIM: A feature similarity index for image quality assessment. IEEE Transactions on Image Processing 20, 8 (2011), 2378--2386.

Digital Library

Cited By

Garde MPatil P(2024)Multi-level thresholding segmentation based on levy horse optimized machine learning approachMultimedia Tools and Applications10.1007/s11042-024-19056-4Online publication date: 24-Apr-2024
https://doi.org/10.1007/s11042-024-19056-4
Mousavirad SSchaefer GZhou HMoghadam M(2023)How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?Knowledge-Based Systems10.1016/j.knosys.2023.110587272(110587)Online publication date: Jul-2023
https://doi.org/10.1016/j.knosys.2023.110587
Liu L(2023)Disease spot image segmentation algorithm with memory-based fruit fly optimization algorithmMultimedia Tools and Applications10.1007/s11042-023-15630-482:30(47135-47163)Online publication date: 4-May-2023
https://doi.org/10.1007/s11042-023-15630-4
Show More Cited By

Index Terms

HCS-BBD: an effective population-based approach for multi-level thresholding
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches

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cover image ACM Conferences

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2021

2047 pages

ISBN:9781450383516

DOI:10.1145/3449726

Editor:
Francisco Chicano
University of Malaga
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General Chair:
Krzysztof Krawiec
Poznan University of Technology

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Published: 08 July 2021

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GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

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Cited By

Garde MPatil P(2024)Multi-level thresholding segmentation based on levy horse optimized machine learning approachMultimedia Tools and Applications10.1007/s11042-024-19056-4Online publication date: 24-Apr-2024
https://doi.org/10.1007/s11042-024-19056-4
Mousavirad SSchaefer GZhou HMoghadam M(2023)How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?Knowledge-Based Systems10.1016/j.knosys.2023.110587272(110587)Online publication date: Jul-2023
https://doi.org/10.1016/j.knosys.2023.110587
Liu L(2023)Disease spot image segmentation algorithm with memory-based fruit fly optimization algorithmMultimedia Tools and Applications10.1007/s11042-023-15630-482:30(47135-47163)Online publication date: 4-May-2023
https://doi.org/10.1007/s11042-023-15630-4
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
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

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