Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey

Authors: C. Deepika, J. Nithya

Abstract:

Segmentation is one of the essential tasks in image processing. Thresholding is one of the simplest techniques for performing image segmentation. Multilevel thresholding is a simple and effective technique. The primary objective of bi-level or multilevel thresholding for image segmentation is to determine a best thresholding value. To achieve multilevel thresholding various techniques has been proposed. A study of some nature inspired metaheuristic algorithms for multilevel thresholding for image segmentation is conducted. Here, we study about Particle swarm optimization (PSO) algorithm, artificial bee colony optimization (ABC), Ant colony optimization (ACO) algorithm and Cuckoo search (CS) algorithm.

Keywords: Ant colony optimization, Artificial bee colony optimization, Cuckoo search algorithm, Image segmentation, Multilevel thresholding, Particle swarm optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096859

References:

[1] H.F. Ng, “Automatic thresholding for defect detection”, pattern Recognition Letters, Volume 27, Issue 14, pp.1644-1649,2006.
[2] Chen Y.L, “Night time Vehicle Light Detection on a Moving Vehicle using Image segmentation and Analysis Techniques”, WSEAS transactions on computers, Volume 8, Issue 3, pp. 506-515,2009.
[3] C.C.Chang, L.L.Wang, “A fast multilevel thresholding method based on lowpass and highpass filtering”, Pattern Recogn. Lett. 18 , 1469- 1478,1997.
[4] X.S. Yang, “Nature-inspired metaheuristic algorithms”, Luniver press, 2008.
[5] Kanika Malik, Akash Tayal, “Comparision of Nature Inspired Metaheuristic Algorithms”, International Journal of Electronic and Electrical Engineering, Volume 7, Number 8, pp. 799-802, 2014.
[6] D.Oliva, E. Cuevas, et al., “Multilevel Thresholding segmentation based on harmony search optimization”, J.Appl.Math, 1-24,2013.
[7] L. Cao, P.Bao,Z.Shi, “The strongest schema learning GA and its application to multilevel Thresholding”, Image Vision Comput. 26 , 716-724, 2008.
[8] W.B. Tao, J.W.Tian, J.Liu, “Image segmentation by three-level Thresholding based on maximum fuzzy entropy and genetic algorithm”, Pattern Recogn. Lett. 24,3069-3078, 2003.
[9] B.Akay,” A study on particle swarm optimization and artificial bee colony algorithms for multilevel Thresholding”, Appl. Soft Comput. 13 ,3066-3091, 2013.
[10] M.Maitra, A.Chatterjee, “A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel Thresholding”, Expert Syst. Appl. 34, 1341-1350, 2008.
[11] P. D. Sathya, R.Kayalvizhi, “Optimal multilevel Thresholding using bacterial foraging algorithm”, Expert Syst. Appl. 38, 15549-15564, 2011.
[12] M. H. Horng, “Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization”, Expert Systems with Applications 37, 4580-4592, 2010.
[13] K.Hammouche, M.Diaf,P.Siarry, “A comparative study of various metaheuristic techniques applied to the multilevel Thresholding problem”, Eng. Appl. Artif. Intell. 23, 676-688, 2010.
[14] Kennedy, J.,rhart, R., “Particle swarm optimization”, In: Proceedings of the IEEE International Conference on Neural Networks (ICNN’95), vol. IV, Perth, Australia, pp. 1942–1948, 1985.
[15] D.Karaboga, “An idea based on honey bee swarm for numerical optimization”, Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department , 2005.
[16] D.Karaboa, B.Batrk, “A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony(ABC) algorithm”, J.Global Optimiz. 39, 459-471, 2007.
[17] D.Karaboa, B.Batrk, “On the performance of Artificial Bee Colony(ABC) algorithm”, Appl. Soft Comput. 8, 687-697, 2008.
[18] P.Civicioglu, E.Besdok, “A conceptual comparision of the cuckoo search, particle swarm optimization, differential evolution and artificial bee colony algorithms”, Artif. Intell. Rev. 39, 315-346, 2013.
[19] Dorigo, M., Gambardella, L.M., “Ant colony system: a cooperative learning approach to the traveling salesman problem”, IEEE transactions on Evolutionary Computation 1 (1), 53-66, 1997.
[20] Dorigo, M., Stutzle, T., “The ant colony optimization metaheuristic: algorithms”, applications and advances. Technical Report IRIDIA-2000- 32, 2000.
[21] Xin-She Yang, Suash Deb, “Engineering Optimisation by Cuckoo Search”,arxiv:1005.2908v3
[math.OC]; 2010.
[22] Yang, X. S., Deb. S., “Cuckoo search via levy flights”, In: Proc. Of World Congress on Nature & Biologically Inspired Computing, pp. 210- 214, 2009.
[23] Chun-Chieh Tseng, Jer-Guang Hsieh, Jyh-Horng Jeng, “Fractal image compression using visual based particle swarm optimization”, Image and Vision Computing 26, 1154–1162, 2008.
[24] Fang Liu, Haibin Duan, Yimin Deng, “A chaotic quantum-behaved particle swarm optimization based on lateral inhibition for image matching”, Optik 123,1955– 1960, 2012.
[25] Jin wei, Zhang jian-qi, Zhang Xiang, “Face recognition method based on support vector machine and particle swarm optimization”, Expert Systems with Applications 38, 4390–4393, 2012.
[26] Xiangyang Wang, Jie Yang, Xialong Teng, Weiijun Xia, Richard Jensen, “Feature selection based on rough sets and particle swarm optimization”, Pattern Recognition Letters 28, 459–471, 2007.
[27] suan – Ying Chen, Jin – Jang Leou, “Saliency-directed image interpolation using particle swarm optimization”, Signal Processing 90, 1676–1692, 2010.
[28] Slami saadi, Abderrezak Guessoum, Maamar Bettayeb, “ABC optimized neural network model for image deblurring with its FPGA implementation”, Microprocessors and Microsystems 37, 52–64, 2013.
[29] Jiaqian Yu, Haibin Duan, “Artificial Bee Colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion”, Optik 124, 3103– 3111, 2013.
[30] Ankush Chakrabarty, Harsh Jain, Amitava Chaterjee, “Volterra kernel based face recognition using artificial bee colony optimization”, Engineering Applications of Artificial Intelligence 26, 1107–1114, 2013.
[31] Li Chen, Xiaotong Huang, Jing Tian, Xiaowei Fu, “Blind noisy image quality evaluation using a deformable ant colony algorithm”, Optics & Laser Technology 57, 265–270, 2014.
[32] Bolun Chen, Ling Chen, Yixin Chen, “Efficient ant colony optimization for image feature selection”, Signal Processing 93, 1566–1576, 2013.
[33] A.K.Bhandari, V.Soni, A.Kumar, G.K.Singh, “Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT– SVD”, ISA Transactions 53 , 1286–1296, 2014.