Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey
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.
[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.
[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.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:68288", author = "C. Deepika and J. Nithya", title = "Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey", 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.", volume = "8", number = "10", pages = "1356-6", }
