Abstract
This paper presents an improved elephant herding optimization (IEHO) to solve the multilevel image thresholding problem for image segmentation by introducing oppositional-based learning (OBL) and dynamic cauchy mutation (DCM). OBL accelerates the convergence rate and enhances the performance of standard EHO whereas DCM mitigates the premature convergence. The suggested optimization approach maximizes two popular objective functions: ‘Kapur’s entropy’ and ‘between-class variance’ to estimate optimized threshold values for segmentation of the image. The performance of the proposed technique is verified on a set of test images taken from the benchmark Berkeley segmentation dataset. The results are analyzed and compared with conventional EHO and other four popular recent metaheuristic algorithms namely cuckoo search, artificial bee colony, bat algorithm, particle swarm optimization and one classical method named dynamic programming found from the literature. Experimental results show that the proposed IEHO provides promising performance compared to other methods in view of optimized fitness value, peak signal-to-noise ratio, structure similarity index and feature similarity index. The suggested algorithm also has better convergence than the other methods taken into consideration.
Similar content being viewed by others
References
Wehrens R, Buydens LMC, Lin Z, Lei Z, Xuanqin M (2000) Classical and nonclassical optimization methods. Encycl Anal Chem 1:9678–9689
Merzban MH, Elbayoumi M (2019) Efficient solution of otsu multilevel image thresholding: a comparative study. Expert Syst Appl 116:299–309
Mousavirad Seyed Jalaleddin, Ebrahimpour-Komleh Hossein (2017) Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms. J Evol Intell 10(1):45–75
Mahesh KM, Renjit A (2018) Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review. J Evol Intell 11(1–2):19–30
Yin PY (1999) A fast scheme for multilevel thresholding using genetic algorithms. Signal Processing 72:85–95
Hammouche K, Diaf M, Siarry P (2008) A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput Vis Image Underst 109(2):163–175
Zhang J, Li H, Tang Z, Lu Q, Zheng X, Zhou J (2014) An improved quantum inspired genetic algorithm for image multilevel thresholding segmentation. Math Problems Eng 112:1–12
Simon D (2008) Biogeography based optimization. IEEE Trans Evol Comput 12(6):702–713
Yin PY (2007) Multilevel minimum cross entropy threshold selection based on particle swarm optimization algorithm. Appl Math Comput 184(2):503–513
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948
Akay B (2013) A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl Soft Comput 13:3066–3091
Bhandari AK, Kumar A, Singh GK (2015) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s Otsu and Tsallis functions. Expert Syst Appl 42:1573–1601
Gandomi AH, Yang XS, Talatahari S, Deb S (2012) Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Comput Math Appl 63(1):191–200
Sathya PD, Kayalvizhi R (2010) Optimum multilevel image thresholding based on Tsallis entropy method with bacterial foraging algorithm. Int J Comput Sci 7(5):336–343
Tao W, Jin H, Liu L (2007) Object segmentation using ant colony optimization algorithm and fuzzy entropy. Pattern Recognit Lett 28(7):788–796
Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30
Horng MH (2010) Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization. Expert Syst Appl 37:4580–4592
Ouadfel S, Taleb-Ahmed A (2016) Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst Appl 55:566–584
Yang XS (2010) A new metaheuristic bat-inspired Algorithm. Stud Comput Intell 284:65–74
Bakhshali MA, Shamsi M (2014) Segmentation of color lip images by optimal thresholding using bacterial foraging optimization (BFO). J Comput Sci 5(2):251–257
Abdul Kayom M, Khairuzzaman SC (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl 86:64–76
El Aziz MA, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82
Wang H, Wu Z, Rahnamayan S, Liu Y, Ventresca M (2011) Enhancing particle swarm optimization using generalized opposition-based learning. Inf Sci 181:4699–4714
Yang X, Huang Z (2012) Opposition-based artificial bee colony with dynamic cauchy mutation for function optimization. Int J Adv Comput Technol 4(4):56–62
Wang GG, Deb S, Gandomi AH, Alavi AH (2016) Opposition-based kill herd algorithm with Cauchy mutation and position clamping. Neurocomputing 177:147–157
Rahnamayan S, Tizhoosh Hamid R, Salama MMA (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1):64–79
Wang GG, Deb S, Geo X-Z, Coelho LDS (2016) A new metaheuristic optimization algorithm motivated by elephant herding behavior. Int J Bio-Inspir Comput 8(6):394–409
Meena NK, Parashar S, Swarnkar A, Gupta N, Niazi KR (2017) Improved elephant herding optimization for multiobjective DER accommodation in distribution systems. IEEE Trans Ind Inf 14(3):1029–1039
Tuba E, Ribic I, Hrosik RC, Tuba M (2017) Support vector machine optimized by elephant herding algorithm for erythemato squamous diseases detection. Information Technology and Quantitative Management (ITQM). Proc Comput Sci 122:916–923
Tizhoosh HR (2006) Opposition-based reinforcement learning. J Adv Comput Intell Intell Inf 10(4):578–585
Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102
Wang H, Liu Y, Zeng SY, Li H, Li C (2007) Opposition-based particle swarm algorithm with Cauchy mutation. In: Proceedings of IEEE congress on evolutionary computation, pp 4750–4756
Wang H, Liu Y, Li C, Zeng S (2007) A hybrid particle swarm algorithm with Cauchy mutation. In: IEEE swarm intelligence symposium, Honolulu, Hawaii, pp 356–360
Rahnamayan S, Tizhoosh HR, Salama M (2008) Opposition versus randomness in soft computing techniques. Appl Soft Comput 8(2):906–918
Rahnamayan S, Tizhoosh Hamid R, Salama MMA (2007) Opposition-based differential evolution(ODE) with variable jumping rate. In: IEEE symposium on foundations of computational intelligence
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans SMC 9(1):62–66
Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Gr Image Process 29:273–285
Zhou W, Alan CB, Hamid SRSR, Eero SP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Lin Z, Lei Z, Xuanqin M, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chakraborty, F., Roy, P.K. & Nandi, D. Oppositional elephant herding optimization with dynamic Cauchy mutation for multilevel image thresholding. Evol. Intel. 12, 445–467 (2019). https://doi.org/10.1007/s12065-019-00238-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-019-00238-1