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High-dimensional multi-level maximum variance threshold selection for image segmentation: a benchmark of recent population-based metaheuristic algorithms

Published: 08 July 2020 Publication History

Abstract

Maximum variance threshold selection (MVTS) has been extensively used in image thresholding. However, conventional MVTS algorithms are time-consuming, in particular when the number of threshold values increases. One approach to moderate this problem is to use population-based metaheuristic algorithms, but, due to the curse of dimensionality, the efficacy of such algorithms may drop sharply in higher-dimensional search spaces. Since various population-based algorithms have been presented in the literature, in this paper, we benchmark the performance of 10 such algorithms for MVTS, focussing in particular on more recent metaheuristics that have received significant attention. As such, the algorithms we evaluate include, apart from the more established differential evolution (DE), and particle swarm optimisation (PSO), artificial bee colony (ABC), the imperialist competitive algorithm (ICA), the grey wolf optimiser (GWO), moth fame optimisation (MFO), the dragonfly algorithm (DA), the sine cosine algorithm (SCA), the multi-verse optimiser (MVO), and the salp swarm algorithm (SSA). We assess these algorithms on a set of benchmark images with regards to objective function value and structural similarity index (SSIM), and also perform a non-parametric statistical test, the Wilcoxon signed-rank test, to compare the algorithms statistically.

References

[1]
E. Atashpaz-Gargari and C. Lucas. 2007. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In IEEE Congress on Evolutionary Computation. 4661--4667.
[2]
H. V. H. Ayala, F. M. dos Santos, V. C. Mariani, and L. dos Santos Coelho. 2015. Image thresholding segmentation based on a novel beta differential evolution approach. Expert Systems with Applications 42, 4 (2015), 2136--2142.
[3]
A. K. Bhandari, A. Kumar, and G. K. Singh. 2015. Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions. Expert Systems with Applications 42, 3 (2015), 1573--1601.
[4]
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.
[5]
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.
[6]
D. Karaboga and B. Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39, 3 (2007), 459--471.
[7]
T. Kurban, P. Civicioglu, R. Kurban, and E. Besdok. 2014. Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding. Applied Soft Computing 23 (2014), 128--143.
[8]
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.
[9]
S. Mirjalili. 2015. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems 89 (2015), 228--249.
[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.
[11]
S. Mirjalili. 2016. SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems 96 (2016), 120--133.
[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.
[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.
[14]
S. Mirjalili, S. M. Mirjalili, and A. Lewis. 2014. Grey wolf optimizer. Advances in Engineering Software 69 (2014), 46--61.
[15]
S. J. Mousavirad and H. Ebrahimpour-Komleh. 2019. Human mental search-based multilevel thresholding for image segmentation. Applied Soft Computing (2019).
[16]
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.
[17]
N. Otsu. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9, 1 (1979), 62--66.
[18]
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.
[19]
Y. Shi and R. Eberhart. 1998. A modified particle swarm optimizer. In IEEE International Conference on Evolutionary Computation. 69--73.
[20]
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.

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  • (2022)Population-based self-adaptive Generalised Masi Entropy for image segmentationKnowledge-Based Systems10.1016/j.knosys.2022.108610245:COnline publication date: 7-Jun-2022
  • (2020)Evolving Feedforward Neural Networks Using a Quasi-Opposition-Based Differential Evolution for Data Classification2020 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI47803.2020.9308591(2320-2326)Online publication date: 1-Dec-2020
  • (2020)CenPSO: A Novel Center-based Particle Swarm Optimization Algorithm for Large-scale Optimization2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283143(2066-2071)Online publication date: 11-Oct-2020
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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
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]

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Published: 08 July 2020

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Author Tags

  1. image histogram
  2. image segmentation
  3. image thresholding
  4. optimisation
  5. population-based algorithms

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Cited By

View all
  • (2022)Population-based self-adaptive Generalised Masi Entropy for image segmentationKnowledge-Based Systems10.1016/j.knosys.2022.108610245:COnline publication date: 7-Jun-2022
  • (2020)Evolving Feedforward Neural Networks Using a Quasi-Opposition-Based Differential Evolution for Data Classification2020 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI47803.2020.9308591(2320-2326)Online publication date: 1-Dec-2020
  • (2020)CenPSO: A Novel Center-based Particle Swarm Optimization Algorithm for Large-scale Optimization2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC42975.2020.9283143(2066-2071)Online publication date: 11-Oct-2020
  • (2020)An Evolutionary Hybrid Feature Selection Approach for Biomedical Data Classification2020 10th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE50421.2020.9303648(623-628)Online publication date: 29-Oct-2020

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