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Efficient image segmentation through 2D histograms and an improved owl search algorithm

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Abstract

Optimization is used in different fields of engineering to solve complex problems. In image processing, multilevel thresholding requires to find the optimal configuration of thresholds to obtain accurate segmented images. In this case, the use of two-dimensional histograms is helpful because they permit us to combine information from the image preserving different features. This paper introduces a new method for multilevel image thresholding segmentation based on the improved version of the owl search algorithm (iOSA) and 2D histograms. The performance of the iOSA is enhanced with the inclusion of a new strategy in the optimization process. Moreover, in the initialization step, it is applied the opposition-based learning. Meanwhile, the 2D histograms permit to maintain more information of the image. Considering such modifications, the iOSA performs a better exploration of the search space during the early iterations, preserving the exploitation of the prominent regions using a self-adaptive variable. The iOSA is employed to allocate the optimal threshold values that segment the image by using the 2D Rényi entropy as an objective function. To test the efficiency of the iOSA, a set of experiments were performed which validate the quality of the segmentation and evaluate the optimization results efficacy. Moreover, to prove that the iOSA is a promising alternative for optimization and image processing problems, statistical tests and analyses were also conducted.

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References

  1. Fausto F, Reyna-Orta A, Cuevas E et al (2019) From ants to whales: metaheuristics for all tastes. Artif Intell Rev. https://doi.org/10.1007/s10462-018-09676-2

    Article  Google Scholar 

  2. Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179:2232–2248. https://doi.org/10.1016/j.ins.2009.03.004

    Article  MATH  Google Scholar 

  3. Mittal H, Saraswat M (2018) An optimum multi-level image thresholding segmentation using non-local means 2D histogram and exponential Kbest gravitational search algorithm. Eng Appl Artif Intell 71:226–235. https://doi.org/10.1016/j.engappai.2018.03.001

    Article  Google Scholar 

  4. Bohat VK, Arya KV (2018) An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks. Knowl Based Syst 143:192–207. https://doi.org/10.1016/j.knosys.2017.12.017

    Article  Google Scholar 

  5. Abedinpourshotorban H, Shamsuddin SM, Beheshti Z, Jawawi DNA (2016) Electromagnetic field optimization: a physics-inspired metaheuristic optimization algorithm. Swarm Evol Comput 26:8–22. https://doi.org/10.1016/j.swevo.2015.07.002

    Article  Google Scholar 

  6. Cuevas E, Echavarría A, Ramírez-Ortegón MA (2014) An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Appl Intell 40:256–272. https://doi.org/10.1007/s10489-013-0458-0

    Article  Google Scholar 

  7. Mirjalili S (2015) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2015.12.022

    Article  Google Scholar 

  8. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68. https://doi.org/10.1177/003754970107600201

    Article  Google Scholar 

  9. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE, pp 4661–4667

  10. Mitchell M (1995) Genetic algorithms: an overview. Complexity 1:31–39. https://doi.org/10.1002/cplx.6130010108

    Article  MATH  Google Scholar 

  11. Eberhart R (1995) Particle swarm optimization. In: IEEE press international conference on JK-P of I

  12. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214:108–132. https://doi.org/10.1016/J.AMC.2009.03.090

    Article  MathSciNet  MATH  Google Scholar 

  13. Cuevas E, Cienfuegos M, Zaldivar D, Perez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40:6374–6384

    Article  Google Scholar 

  14. Jain M, Maurya S, Rani A, Singh V (2018) Owl search algorithm: a novel nature-inspired heuristic paradigm for global optimization. J Intell Fuzzy Syst 34:1573–1582. https://doi.org/10.3233/JIFS-169452

    Article  Google Scholar 

  15. Tang R, Fong S (2018) Clustering big IoT data by metaheuristic optimized mini-batch and parallel partition-based DGC in Hadoop. Futur Gener Comput Syst 86:1395–1412. https://doi.org/10.1016/J.FUTURE.2018.03.006

    Article  Google Scholar 

  16. Abedinia O, Amjady N, Ghadimi N (2018) Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput Intell 34:241–260. https://doi.org/10.1111/coin.12145

    Article  MathSciNet  Google Scholar 

  17. Rabbani M, Sabbaghnia A, Mobini M, Razmi J (2018) A graph theory-based algorithm for a multi-echelon multi-period responsive supply chain network design with lateral-transshipments. Oper Res. https://doi.org/10.1007/s12351-018-0425-y

    Article  Google Scholar 

  18. Doering J, Kizys R, Juan AA et al (2019) Metaheuristics for rich portfolio optimisation and risk management: current state and future trends. Oper Res Perspect. https://doi.org/10.1016/J.ORP.2019.100121

    Article  MathSciNet  Google Scholar 

  19. Hoang N-D, Tran V-D (2019) Image processing-based detection of pipe corrosion using texture analysis and metaheuristic-optimized machine learning approach. Comput Intell Neurosci 2019:1–13. https://doi.org/10.1155/2019/8097213

    Article  Google Scholar 

  20. Djemame S, Batouche M, Oulhadj H, Siarry P (2019) Solving reverse emergence with quantum PSO application to image processing. Soft Comput 23:6921–6935. https://doi.org/10.1007/s00500-018-3331-6

    Article  Google Scholar 

  21. Amiri Golilarz N, Gao H, Demirel H (2019) Satellite image de-noising with Harris hawks meta heuristic optimization algorithm and improved adaptive generalized Gaussian distribution threshold function. IEEE Access 7:57459–57468. https://doi.org/10.1109/ACCESS.2019.2914101

    Article  Google Scholar 

  22. Hinojosa S, Pajares G, Cuevas E, Ortega-Sanchez N (2018) Thermal image segmentation using evolutionary computation techniques. Stud Comput Intell 730:63–88. https://doi.org/10.1007/978-3-319-63754-9_4

    Article  Google Scholar 

  23. Garcia-Garcia A, Orts-Escolano S, Oprea S et al (2018) A survey on deep learning techniques for image and video semantic segmentation. Appl Soft Comput 70:41–65. https://doi.org/10.1016/J.ASOC.2018.05.018

    Article  Google Scholar 

  24. Suresh K, Srinivasa Rao P (2019) Various image segmentation algorithms: a survey. Springer, Singapore, pp 233–239

    Google Scholar 

  25. Chouhan SS, Kaul A, Singh UP (2018) Soft computing approaches for image segmentation: a survey. Multimed Tools Appl 77:28483–28537. https://doi.org/10.1007/s11042-018-6005-6

    Article  Google Scholar 

  26. Tian K, Li J, Zeng J et al (2019) Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Comput Electron Agric. https://doi.org/10.1016/J.COMPAG.2019.104962

    Article  Google Scholar 

  27. Elaziz MA, Oliva D, Ewees AA, Xiong S (2019) Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer. Expert Syst Appl 125:112–129. https://doi.org/10.1016/j.eswa.2019.01.047

    Article  Google Scholar 

  28. Díaz-Cortés MA, Ortega-Sánchez N, Hinojosa S et al (2018) A multi-level thresholding method for breast thermograms analysis using Dragonfly algorithm. Infrared Phys Technol 93:346–361. https://doi.org/10.1016/j.infrared.2018.08.007

    Article  Google Scholar 

  29. Hinojosa S, Avalos O, Oliva D et al (2018) Unassisted thresholding based on multi-objective evolutionary algorithms. Knowl Based Syst 159:221–232. https://doi.org/10.1016/j.knosys.2018.06.028

    Article  Google Scholar 

  30. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13:146. https://doi.org/10.1117/1.1631315

    Article  Google Scholar 

  31. Abutaleb AS (1989) Automatic thresholding of gray-level pictures using two-dimensional entropy. Comput Vis Graph Image Process 47:22–32. https://doi.org/10.1016/0734-189X(89)90051-0

    Article  Google Scholar 

  32. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: Proceedings—2005 IEEE computer society conference on computer vision and pattern recognition, CVPR 2005. IEEE, pp 60–65

  33. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66. https://doi.org/10.1109/TSMC.1979.4310076

    Article  Google Scholar 

  34. Sarkar S, Das S, Chaudhuri SS (2015) A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution. Pattern Recognit Lett 54:27–35. https://doi.org/10.1016/J.PATREC.2014.11.009

    Article  Google Scholar 

  35. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vis Graph Image Process 29:273–285

    Article  Google Scholar 

  36. de Portes Albuquerque M, Esquef IA, Gesualdi Mello AR, Portes de Albuquerque M (2004) Image thresholding using Tsallis entropy. Pattern Recognit Lett 25:1059–1065. https://doi.org/10.1016/j.patrec.2004.03.003

    Article  Google Scholar 

  37. Życzkowski K (2003) Rényi extrapolation of Shannon entropy. Open Syst Inf Dyn 10:297–310. https://doi.org/10.1023/A:1025128024427

    Article  MathSciNet  MATH  Google Scholar 

  38. Brink AD, Pendock NE (1996) Minimum cross-entropy threshold selection. Pattern Recognit 29:179–188. https://doi.org/10.1016/0031-3203(95)00066-6

    Article  Google Scholar 

  39. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5:3. https://doi.org/10.1145/584091.584093

    Article  Google Scholar 

  40. Sahoo PK, Arora G (2004) A thresholding method based on two-dimensional Renyi’s entropy. Pattern Recognit 37:1149–1161. https://doi.org/10.1016/j.patcog.2003.10.008

    Article  MATH  Google Scholar 

  41. Cheng C, Hao X, Liu S (2014) Image segmentation based on 2D Renyi gray entropy and fuzzy clustering. In: 2014 12th International conference on signal processing (ICSP). IEEE, pp 738–742

  42. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67

    Article  Google Scholar 

  43. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE international conference on computer vision. IEEE computer society, pp 416–423

  44. Wang X, Wang H, Yang J, Zhang Y (2016) A new method for nonlocal means image denoising using multiple images. PLoS One 11:e0158664. https://doi.org/10.1371/journal.pone.0158664

    Article  Google Scholar 

  45. Xu M, Shang P, Zhang S (2019) Multiscale analysis of financial time series by Rényi distribution entropy. Phys A Stat Mech Appl. https://doi.org/10.1016/J.PHYSA.2019.04.152

    Article  Google Scholar 

  46. Jauregui M, Zunino L, Lenzi EK et al (2018) Characterization of time series via Rényi complexity—entropy curves. Phys A Stat Mech Appl 498:74–85. https://doi.org/10.1016/J.PHYSA.2018.01.026

    Article  Google Scholar 

  47. Ben Ishak A (2017) A two-dimensional multilevel thresholding method for image segmentation. Appl Soft Comput 52:306–322. https://doi.org/10.1016/J.ASOC.2016.10.034

    Article  Google Scholar 

  48. Hughes MS, Marsh JN, Arbeit JM et al (2009) Application of Renyi entropy for ultrasonic molecular imaging. J Acoust Soc Am 125:3141–3145. https://doi.org/10.1121/1.3097489

    Article  Google Scholar 

  49. Koltcov S (2018) Application of Rényi and Tsallis entropies to topic modeling optimization. Phys A Stat Mech Appl 512:1192–1204. https://doi.org/10.1016/J.PHYSA.2018.08.050

    Article  Google Scholar 

  50. Li T, Guo S (2017) Research on two-dimensional entropy threshold method based on improved genetic algorithm. In: 2017 International conference on industrial informatics—computing technology, intelligent technology, industrial information integration (ICIICII). IEEE, pp 122–125

  51. Borjigin S, Sahoo PK (2019) Color image segmentation based on multi-level Tsallis–Havrda–Charvát entropy and 2D histogram using PSO algorithms. Pattern Recognit 92:107–118. https://doi.org/10.1016/J.PATCOG.2019.03.011

    Article  Google Scholar 

  52. Yi S, Zhang G, He J, Tong L (2019) Entropic image thresholding segmentation based on Gabor histogram. KSII Trans Internet Inf Syst 13:89. https://doi.org/10.3837/tiis.2019.04.021

    Article  Google Scholar 

  53. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06) (vol 1, pp 695–701). IEEE

  54. Mahdavi S, Rahnamayan S, Deb K (2018) Opposition based learning: a literature review. Swarm Evol Comput 39:1–23. https://doi.org/10.1016/j.swevo.2017.09.010

    Article  Google Scholar 

  55. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  56. Li Q, Liu SY, Yang XS (2020) Influence of initialization on the performance of metaheuristic optimizers. Appl Soft Comput J 91:106193. https://doi.org/10.1016/j.asoc.2020.106193

    Article  Google Scholar 

  57. Varnan CS, Jagan A, Kaur J et al (2011) Image quality assessment techniques in spatial domain. Int J Comput Sci Technol 2:177–184

    Google Scholar 

  58. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612. https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  59. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20:2378–2386. https://doi.org/10.1109/TIP.2011.2109730

    Article  MathSciNet  MATH  Google Scholar 

  60. Memon FA, Unar MA, Memon S (2016) Image Quality assessment for performance evaluation of focus measure operators. ArXiv abs/1604.0

  61. Oliva D, Elaziz MA, Hinojosa S (2019) Metaheuristic algorithms for image segmentation: theory and applications. Springer, Cham

    Book  Google Scholar 

  62. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proc Eighth IEEE Int Conf Comput Vision ICCV 2:416–423. https://doi.org/10.1109/ICCV.2001.937655

    Article  Google Scholar 

  63. Theodorsson-Norheim E (1986) Kruskal–Wallis test: BASIC computer program to perform nonparametric one-way analysis of variance and multiple comparisons on ranks of several independent samples. Comput Methods Programs Biomed 23:57–62. https://doi.org/10.1016/0169-2607(86)90081-7

    Article  Google Scholar 

  64. St L, Wold S (1989) Analysis of variance (ANOVA). Chemom Intell Lab Syst 6:259–272. https://doi.org/10.1016/0169-7439(89)80095-4

    Article  Google Scholar 

  65. Hájek J, Šidák Z, Sen PK et al (1999) Selected rank tests. Theory Rank Tests. https://doi.org/10.1016/B978-012642350-1/50022-9

    Article  MATH  Google Scholar 

  66. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18. https://doi.org/10.1016/j.swevo.2011.02.002

    Article  Google Scholar 

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Authors and Affiliations

  1. Departamento de Ingenierías, Universidad de Guadalajara, CUCSUR, Av. Independencia Nacional 151, Autlán de Navarro, Jalisco, Mexico

    Andrea H. del Río

  2. División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal, Mexico

    Itzel Aranguren, Diego Oliva & Erik Cuevas

  3. Computer Science Department, Universitat Oberta de Catalunya, Castelldefels, Spain

    Diego Oliva

  4. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed Abd Elaziz

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  1. Andrea H. del Río
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  2. Itzel Aranguren
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  3. Diego Oliva
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  4. Mohamed Abd Elaziz
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  5. Erik Cuevas
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del Río, A.H., Aranguren, I., Oliva, D. et al. Efficient image segmentation through 2D histograms and an improved owl search algorithm. Int. J. Mach. Learn. & Cyber. 12, 131–150 (2021). https://doi.org/10.1007/s13042-020-01161-z

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