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A survey on the applications of artificial bee colony in signal, image, and video processing

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Abstract

Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm, which simulates the foraging behavior of honeybees. It has been successfully applied to many optimization problems in different areas. Since 2009, ABC algorithm has been employed for various problems in signal, image, and video processing fields. This paper presents the problems ABC algorithm has been applied in these fields and describes how ABC algorithm was used in the approaches for solving these kinds of problems.

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References

  1. Agrawal, S., Soni, S.: Noisy image segmentation based on genetic artificial bee colony algorithm. Int. J. Comput. Sci. Eng. Technol. 5(7), 754–763 (2014)

    Google Scholar 

  2. Ahirwal, M.K., Kumar, A., Singh, G.K.: Adaptive filtering of EEG/ERP through bounded range artificial bee colony (BR-ABC) algorithm. Digital Signal Process. 25, 164–172 (2014)

    Article  Google Scholar 

  3. Akay, B., Karaboga, D.: Wavelet packets optimization using artificial bee colony algorithm. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 89–94 (June 2011)

  4. Akay, B., Kirmizi, I.: Structural optimization of wavelet packets using swarm algorithms. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–5 (June 2012)

  5. Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013)

    Article  Google Scholar 

  6. Akay, B.: Synchronous and asynchronous pareto-based multi-objective artificial bee colony algorithms. J. Global Optim. 57(2), 415–445 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  7. Alrosan, A., Norwawi, N., Ismail, W., Alomoush, W.: Artificial bee colony based fuzzy clustering algorithms for MRI image segmentation. In: International Conference on Advances in Computer Science and Electronics Engineering—CSEE 2014, pp. 225–228 (2014)

  8. Armano, G., Farmani, M.R.: Clustering analysis with combination of artificial bee colony algorithm and k-means technique. Int. J. Comput. Theory Eng. 6(2), 141–145 (2014)

    Article  Google Scholar 

  9. Arora, P., Kundra, H., Panchal, V.: Fusion of biogeography based optimization and artificial bee colony for identification of natural terrain features. Int. J. Adv. Comput. Sci. Appl. 3(10), 107–111 (2012)

    Google Scholar 

  10. Balasubramani, K., Marcus, K.: Artificial bee colonyalgorithm to improve brain MR image segmentation. A.B.C.A. Int. J. Comput. Sci. Eng. (IJCSE) 5(1), 31–36 (2013)

    Google Scholar 

  11. Banerjee, S., Bharadwaj, A., Gupta, D., Panchal, V.K.: Remote sensing image classification using artificial bee colony algorithm. Int. J. Comput. Sci. Inf. 2(3), 67–72 (2012)

    Google Scholar 

  12. Banharnsakun, A., Tanathong, S.: Object detection based on template matching through use of best-so-far ABC. Comput. Intell. Neurosci. 2014, 1–8 (2014)

    Article  Google Scholar 

  13. Bansal, J.C., Sharma, H., Jadon, S.S.: Artificial bee colony algorithm: a survey. Int. J. Adv. Intell. Paradig. 5(1), 123–159 (2013)

  14. Basturk, B., Karaboga, D.: An artificial bee colony (abc) algorithm for numeric function optimization. In: IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA (May 2006)

  15. Benala, T., Jampala, S., Villa, S., Konathala, B.: A novel approach to image edge enhancement using artificial bee colony optimization algorithm for hybridized smoothening filters. In: NaBIC 2009. World Congress on Nature Biologically Inspired Computing, 2009, pp. 1071–1076 (Dec 2009)

  16. Beno, M.M., George, A., Valarmathi, I.R., Swamy, S.M.: Hybrid optimzation model of video steganography technique with the aid of biorthogonal wavelet transform. J. Theor. Appl. Inf. Technol. 63(1), 190–199 (2014)

    Google Scholar 

  17. Beno, M.M., Valarmathi, I.R., Swamy, S.M., Rajakumar, B.R.: Threshold prediction for segmenting tumour from brain MRI scans. Int. J. Imaging Syst. Technol. 24(2), 129–137 (2014)

    Article  Google Scholar 

  18. Bhandari, A.K., Soni, V., Kumar, A., Singh, G.K.: Artificial bee colony-based satellite image contrast and brightness enhancement technique using DWT-SVD. Int. J. Remote Sens. 35(5), 1601–1624 (2014)

    Article  Google Scholar 

  19. Bolaji, A.L., Khader, A.T., Al-betar, M.A., Awadallah, M.A.: Artificial bee colony algorithm, its variants and applications: a survey. J. Theor. Appl. Inf. Technol. 47(2), 434–459 (2013)

    Google Scholar 

  20. Cagnon, S., Lutto, E., Olagu, G.: Genetic and Evolutionary Computation for Image Processing and Analysis, 1st edn. Hindawi Publishing Corp, New York, NY (2008)

    Google Scholar 

  21. Chakrabarty, A., Jain, H., Chatterjee, A.: Volterra kernel based face recognition using artificial bee colony optimization. Eng. Appl. Artif. Intell. 26(3), 1107–1114 (2013)

    Article  Google Scholar 

  22. Chandrakala, D., Sumathi, S.: Application of artificial bee colony optimization algorithm for image classification using color and texture feature similarity fusion. ISRN Artificial Intelligence 2012 (1–10) (2012)

  23. Chandrakala, D., Sumathi, S.: Image classification based on color and texture features using frbfn network with artificial bee colony optimization algorithm. Int. J. Comput. Appl. 98(14), 19–29 (2014)

    Google Scholar 

  24. Charansiriphaisan, K., Chiewchanwattana, S., Sunat, K.: A comparative study of improved artificial bee colony algorithms applied to multilevel image thresholding. Mathematical Problems in Engineering 2013, 1–17 (2013)

  25. Chatterjee, A., Tudu, B., Paul, K.: Binary grayscale halftone pattern generation using binary artificial bee colony (bABC). SIViP 7(6), 1195–1209 (2013)

    Article  Google Scholar 

  26. Chen, L., Zhang, L., Guo, Y.: Blind Image Separation Method Based on Artificial Bee Colony Algorithm. In: Chen, W.Z., Dai, P.Q., Chen, Y.L., Chen, D.N., Jiang, Z.Y. (eds.) AUTOMATION EQUIPMENT AND SYSTEMS, PTS 1–4. Advanced Materials Research, vol. 468–471, pp. 583–586. Fujian Univ Technol; Xiamen Univ; Fuzhou Univ; Huaqiao Univ; Univ Wollongong; Fujian Mech Engn Soc; Hong Kong Ind Technol Res Ctr (2012), 3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012), Xiamen, PEOPLES R CHINA, MAR 27–29 (2012)

  27. Chen, Y., Zhang, J., Wang, S., Zheng, Y.: Brain magnetic resonance image segmentation based on an adapted non-local fuzzy c-means method. IET Comput. Vis. 6(6), 610–625 (2012)

    Article  MathSciNet  Google Scholar 

  28. Chen, Y., Yu, W., Feng, J.: A reliable svd-dwt based watermarking scheme with artificial bee colony algorithm. Int. J. Digital Content Technol. Appl. 6(22), 430–439 (2012)

    Article  Google Scholar 

  29. Chidambaram, C., Lopes, H.: A new approach for template matching in digital images using an artificial bee colony algorithm. In: World Congress on Nature Biologically Inspired Computing, 2009. NaBIC 2009, pp. 146–151 (Dec 2009)

  30. Chidambaram, C., Lopes, H.S.: An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching. In: Nature-Inspired Computing Design, Development, and Applications, pp. 141–157, Ch. 8. IGI Global (2012)

  31. Chidambaram, C., Maral, M., Dorini, L., Vieira Neto, H., Lopes, H.S.: An improved abc algorithm approach using surf for face identification. In: Yin, H., Costa, J.A.F., Barreto, G. (eds.) Intelligent Data Engineering and Automated Learning—IDEAL 2012, Lecture Notes in Computer Science, vol. 7435, pp. 143–150. Springer, Berlin Heidelberg (2012)

  32. Chidambaram, C., Lopes, H.: An improved artificial bee colony algorithm for the object recognition problem in complex digital images using template matching. Int. J. Nat. Comput. Res. 1(2), 54–70 (2010)

    Article  Google Scholar 

  33. Cuevas, E., Sencin, F., Zaldivar, D., Prez-Cisneros, M., Sossa, H.: A multi-threshold segmentation approach based on artificial bee colony optimization. Appl. Intell. 37(3), 321–336 (2012)

    Article  Google Scholar 

  34. Cuevas, E., Sención-Echauri, F., Zaldivar, D., Pérez-Cisneros, M.: Multi-circle detection on images using artificial bee colony (ABC) optimization. Soft. Comput. 16(2), 281–296 (2012)

    Article  Google Scholar 

  35. Cuevas, E., Zaldivar, D., Perez-Cisneros, M., Sossa, H., Osuna, V.: Block matching algorithm for motion estimation based on artificial bee colony (ABC). Appl. Soft Comput. 13(6), 3047–3059 (2013)

    Article  Google Scholar 

  36. Demir, K.: Color Map Quantization by Using Artificial Intelligence Techniques. Master’s thesis, Erciyes University, Turkiye (2014)

  37. Deng, Y., Duan, H.: Biological edge detection for UCAV via improved artificial bee colony and visual attention. Aircr. Eng. Aerosp. Technol. 86(2), 138–146 (2014)

    Article  Google Scholar 

  38. Dilmac, S., Korurek, M.: A new ecg arrhythmia clustering method based on modified artificial bee colony algorithm, comparison with ga and pso classifiers. In: 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–5 (2013)

  39. Dorigo, M.: Optimization, Learning and Natural Algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)

  40. Duan, H., Deng, Y., Wang, X., Chunfang, X.: Small and dim target detection via lateral inhibition filtering and artificial bee colony based selective visual attention. Plos One 8(8), 1–12 (2013)

    Article  Google Scholar 

  41. Durmus, B., Ozyon, S., Aydin, D., Kuvat, G.: IIR filter design using incremental artificial bee colony with powell’s CDS. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–5 (2012)

  42. Emary, E., Zawbaa, H., Hassanien, A., Schaefer, G., Azar, A.: Retinal blood vessel segmentation using bee colony optimisation and pattern search. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1001–1006 (2014)

  43. Fazli, S., Ghiri, S.F.: Automatic circle detection in digital images using artificial bee colony algorithm. In: International Conference on Advances in Computer and Electrical Engineering. Manila, Philippines, pp. 21–24 (2012)

  44. Gayathri, R., Pavithra, N., Preethi, V.: Artificial bee colony based multifeature recognition. Int. J. Comput. Sci. Issues 11(2), 152–159 (2014)

    Google Scholar 

  45. Gondalia, N., Joshi, F., Mankad, N.: A novel approach of image ranking based on enhanced artificial bee colony algorithm. Int. J. Sci. Res. Dev 1(9), 1767–1771 (2013)

    Google Scholar 

  46. Gupta, T., Kumar, D.: Optimization of clustering problem using population based artificial bee colony algorithm: a review. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(4), 491–502 (2014)

    Google Scholar 

  47. Hanbay, K., Talu, M., Karci, A.: Segmentation of color texture images with artificial bee colony algorithm and wavelet transform. In: 2012 20th on Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2012)

  48. Hanbay, K., Talu, M.F.: Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set. Appl. Soft Comput. 21, 433–443 (2014), http://www.sciencedirect.com/science/article/pii/S1568494614001732

  49. Hancer, E., Ozturk, C., Karaboga, D.: Artificial bee colony based image clustering method. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–5 (2012)

  50. Hancer, E., Ozturk, C., Karaboga, D.: Extraction of brain tumors from MRI images with artificial bee colony based segmentation methodology. In: 2013 8th International Conference on Electrical and Electronics Engineering (ELECO), pp. 516–520 (2013)

  51. Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  52. Horng, M.H., Jiang, T.W.: The artificial bee colony algorithm for vector quantization in image compression. In: 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), pp. 319–323 (2011)

  53. Horng, M.H.: Multilevel minimum cross entropy image thresholding using artificial bee colony algorithm. TELKOMNIKA Indones. J. Electr. Eng. 11(9), 5229–5236 (2013). http://iaesjournal.com/online/index.php/TELKOMNIKA/article/view/3273

  54. Horng, M.H., Jiang, T.W.: Multilevel image thresholding selection using the artificial bee colony algorithm. In: Wang, F., Deng, H., Gao, Y., Lei, J. (eds.) Artificial Intelligence and Computational Intelligence. Lecture Notes in Computer Science, vol. 6320, pp. 318–325. Springer, Berlin Heidelberg (2010)

    Chapter  Google Scholar 

  55. Horng, M.H.: Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst. Appl. 38(11), 13785–13791 (2011)

    Google Scholar 

  56. Hung, C.C., Casper, E., Kuo, B.C., Liu, W., Jung, E., Yang, M.: A quantum-modeled artificial bee colony clustering algorithm for remotely sensed multi-band image segmentation. In: 2013 IEEE International on Geoscience and Remote Sensing Symposium (IGARSS), pp. 2585–2588 (2013)

  57. Ismail, M.M., Baskaran, K.: Hybrid lifting based image compression scheme using particle swarm optimization algorithm and artificial bee colony algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 3(1), 4899–4907 (2014)

    Google Scholar 

  58. Jain, S.N., Rai, C.S.: Blind source separation of super and sub-Gaussian signals with ABC algorithm. ACEEE Int. J. Signal Image Process. 5(1), 10 (2014)

    Google Scholar 

  59. Jia, C.: Change detection in remote sensing images based on the fuzzy clustering algorithm and artificial beecolony optimization. Electron. Sci. Technol. 25(11), 11–14 (2012)

    Google Scholar 

  60. Jianhui, L., Miao, M.: Artificial bee colony algorithm based research on image segmentation. Comput. Eng. Appl. 48(8), 194–196 (2012)

    Google Scholar 

  61. Karaboga, D., Akay, B.: Solving large scale numerical problems using artificial bee colony algorithm. In: 6th International Symposium on Intelligent and Manufacturing Systems Features, Strategies and Innovation, pp. 401–410, Sakarya, Turkiye, 14–17 Oct, 2008

  62. Karaboga, D., Basturk, B.: Artificial bee colony (ABC) Optimization algorithm for solving constrained optimization problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, LNCS, vol 4529/2007, pp. 789–798. Springer, Berlin (2007). doi:10.1007/978-3-540-72950-1_77

    Chapter  Google Scholar 

  63. Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev., 1–37 (2012)

  64. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Tech. Rep. TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

  65. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39(3), 459–471 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  66. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  67. Karaboga, D., Akay, B.: A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 214, 108–132 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  68. Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31(1–4), 61–85 (2009)

    Article  Google Scholar 

  69. Karaboga, N.: A new design method based on artificial bee colony algorithm for digital IIR filters. J. Frankl. Inst. 346(4), 328–348 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  70. Karaboga, D.: Artificial bee colony algorithm. Scholarpedia 5(3), 6915 (2010)

    Article  Google Scholar 

  71. Karaboga, D., Akay, B.: A modified artificial bee colony (abc) algorithm for constrained optimization problems. Appl. Soft Comput. 11(3), 3021–3031 (2011)

    Article  Google Scholar 

  72. Karaboga, N., Cetinkaya, M.B.: A novel and efficient algorithm for adaptive filtering: artificial bee colony algorithm. Turk. J. Electr. Eng. Comput. Sci. 19(1), 175–190 (2011)

    Google Scholar 

  73. Karaboga, N., Latifoglu, F.: Adaptive filtering noisy transcranial doppler signal by using artificial bee colony algorithm. Eng. Appl. Artif. Intell. 26(2), 677–684 (2013)

    Article  Google Scholar 

  74. Karaboga, N., Latifoglu, F.: Elimination of noise on transcranial doppler signal using IIR filters designed with artificial bee colony—ABC-algorithm. Digital Signal Proc. 23(3), 1051–1058 (2013)

    Article  MathSciNet  Google Scholar 

  75. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, 1995. Proceedings., vol. 4, pp. 1942–1948 (1995)

  76. Kockanat, S., Karaboga, N., Koza, T.: Image denoising with 2-d fir filter by using artificial bee colony algorithm. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–4 (2012)

  77. Kockanat, S., Karaboga, N.: Parameter tuning of artificial bee colony algorithm for gaussian noise elimination on digital images. In: 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–4 (2013)

  78. Koza, T., Karaboga, N., Kockanat, S.: Aort valve doppler signal noise elimination using IIR filter designed with ABC algorithm. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications (INISTA), pp. 1–5 (2012)

  79. Koza, J.R.: Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems. Tech. rep., Stanford University Computer Science Department Technical Report STAN-CS-90-1314 (1990)

  80. Kumar, S., Sharma, T.K., Pant, M., Ray, A.: Adaptive artificial bee colony for segmentation of CT lung images. In: International Conference on Recent Advances and Future Trends in Information Technology (iRAFIT2012), International Journal of Computer Applications (IJCA), pp. 1–5 (2012)

  81. Kumar, S., Kumar, P., Sharma, T., Pant, M.: Bi-level thresholding using PSO, artificial bee colony and MRLDE embedded with Otsu method. Memet. Comput. 5(4), 323–334 (2013)

    Article  Google Scholar 

  82. Latifoglu, F.: A novel approach to speckle noise filtering based on artificial bee colony algorithm: an ultrasound image application. Comput. Methods Progr. Biomed. 111(3), 561–569 (2013)

    Article  MathSciNet  Google Scholar 

  83. Lei, C., Liyi, Z., Yanju, G., Ting, L., Qiang, L.: Moving target detection method based on artificial bee colony algorithm. Comput. Eng. Appl. 48(21), 178–181 (2012)

    Google Scholar 

  84. Li, C., Chan, F.: Complex-fuzzy adaptive image restoration—an artificial-bee-colony-based learning approach. In: Nguyen, N., Kim, C.G., Janiak, A. (eds.) Intelligent Information and Database Systems. Lecture Notes in Computer Science, vol. 6592, pp. 90–99. Springer, Berlin Heidelberg (2011)

    Chapter  Google Scholar 

  85. Li, X., Li, L.J.: Preference multi-objective artificial bee colony and its application in camellia fruit image recognition. Appl. Res. Comput. 29(12), 4779–4781 (2012)

    Google Scholar 

  86. Li, B., Gong, L.G., Li, Y.: A novel artificial bee colony algorithm based on internal-feedback strategy for image template matching. Sci. World J. 2014, 1–14 (2014)

    Google Scholar 

  87. Lin, J., Wu, S.: Fuzzy artificial bee colony system with cooling schedule for the segmentation of medical images by using of spatial information. Res. J. Appl. Sci. Eng. Technol. 4(17), 2973–2980 (2012)

    MathSciNet  Google Scholar 

  88. Liu, Y., Hu, K., Zhu, Y., Chen, H.: A novel method for image segmentation based on nature inspired algorithm. In: Huang, D.S., Han, K., Gromiha, M. (eds.) Intelligent Computing in Bioinformatics. Lecture Notes in Computer Science, vol. 8590, pp. 390–402. Springer International Publishing, Berlin (2014)

    Chapter  Google Scholar 

  89. Fogel, L.J.: Autonomous automata. Ind. Res. 4, 14–19 (1962)

    Google Scholar 

  90. Ma, M., Liang, J., Guo, M., Fan, Y., Yin, Y.: SAR image segmentation based on artificial bee colony algorithm. Appl. Soft Comput. 11(8), 5205–5214 (2011)

    Article  Google Scholar 

  91. Madhansubramanian, Tamilarasi, M.: Multioriented video scene based image dehazing using artificial bee colony optimization. Int. J. Innov. Res. Dev. 3(4), 255–260 (2014)

    Google Scholar 

  92. Malekzadeh, M., Khosravi, A., Alighale, S., Azami, H.: Optimization of orthogonal poly phase coding waveform based on bees algorithm and artificial bee colony for MIMO radar. In: Huang, D.-S., Jiang, C., Bevilacqua, V., Figueroa, J. (eds.) Intelligent Computing Technology. Lecture Notes in Computer Science, vol. 7389, pp. 95–102. Springer, Berlin Heidelberg (2012). doi:10.1007/978-3-642-31588-6_13

    Chapter  Google Scholar 

  93. Manda, K., Satapathy, S.C., Rao, K.R.: Artificial bee colony based image clustering. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds.) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol. 132, pp. 29–37. Springer, Berlin Heidelberg (2012)

    Google Scholar 

  94. George, M.M., Karnan, M., Sivakumar, R.: Supervised artificial bee colony system for tumor segmentation in CT/MRI images. Int. J. Comput. Sci. Manag. Res. 2(5), 2529–2533 (2013)

    Google Scholar 

  95. Ming, Y., Yue-qiao, A.: SVM parameters optimization based on artificial bee colony algorithm and its application in handwriting verification. In: 2011 International Conference on Electrical and Control Engineering (ICECE), pp. 5026–5029 (2011)

  96. Mishra, A., Das, M., Panda, T.: Multi-objective artificial bee colony (moABC) algorithm to improve content-based image retrieval performance. J. Theor. Appl. Inf. Technol. 59(3), 745–758 (2014)

    Google Scholar 

  97. Mohamed Mansoor Roomi, S., Bhargavi, R., Bhumesh, S.: Visual model based single image dehazing using artificial bee colony optimization. Int. J. Inf. Sci. Tech. (IJIST) 2(3), 77–88 (2012)

    Google Scholar 

  98. Mohammadi, F.G., Abadeh, M.S.: Image steganalysis using a bee colony based feature selection algorithm. Eng. Appl. Artif. Intell. 31(SI), 35–43 (2014)

    Article  Google Scholar 

  99. Ouadfel, S., Meshoul, S.: Handling fuzzy image clustering with a modified ABC algorithm. Int. J. Intell. Syst. Appl. 4(12), 65–74 (2012)

    Google Scholar 

  100. Ouadfel, S., Meshoul, S.: Bio-inspired algorithms for multilevel image thresholding. Int. J. Comput. Appl. Technol. 49(3/4), 207–226 (2014)

    Article  Google Scholar 

  101. Ozturk, C., Hancer, E., Karaboga, D.: Improved clustering criterion for image clustering with artificial bee colony algorithm. Pattern Anal. Appl., 1–13 (2014)

  102. Ozturk, C., Hancer, E., Karaboga, D.: Color image quantization: a short review and an application with artificial bee colony algorithm. Informatica 25(3), 485–503 (2014)

    Google Scholar 

  103. Parmaksizoglu, S., Alci, M.: A novel cloning template designing method by using an artificial bee colony algorithm for edge detection of CNN based imaging sensors. Sensors 11(5), 5337–5359 (2011)

    Article  Google Scholar 

  104. Praveena, S., Sing, S.: Hybrid clustering algorithm and feed-forward neural network for satellite image classification. Int. J. Eng. Sci. Invent. 3(1), 39–47 (2014)

    Google Scholar 

  105. Rahkar-Farshi, T., Kesemen, O., Behjat-Jamal, S.: Multi hyperbole detection on images using modified artificial bee colony (ABC) for multimodal function optimization. In: 2014 22nd Signal Processing and Communications Applications Conference (SIU), pp. 894–898 (2014)

  106. Ramanathan, R., Kalaiarasi, K., Prabha, D.: Improved wavelet based compression with adaptive lifting scheme using artificial bee colony algorithm. Int. J. Adv. Res. Comput. Eng. Technol. 2(4), 1549–1554 (2013)

    Google Scholar 

  107. Rechenberg, I.: Cybernetic solution path of an experimental problem. Tech. rep., Royal Aircraft Establishment, arnborough p. Library Translation F 1122 (1965)

  108. Saadi, S., Bettayeb, M., Guessoum, A., Abdelhafidi, M.: Artificial bees colony optimized neural network model for ecg signals classification. In: Huang, T., Zeng, Z., Li, C., Leung, C. (eds.) Neural Information Processing. Lecture Notes in Computer Science, vol. 7666, pp. 339–346. Springer, Berlin Heidelberg (2012)

    Chapter  Google Scholar 

  109. Saadi, S., Guessoum, A., Bettayeb, M.: ABC optimized neural network model for image deblurring with its FPGA implementation. Microprocess. Microsyst. 37(1), 52–64 (2013)

    Article  Google Scholar 

  110. Saeedi, S., Samadzadegan, F., El-Sheimy, N.: Object extraction from lidar data using an artificial swarm bee colony clustering algorithm. In: Stilla, U., Rottensteiner, F.P.N. (eds.) CMRT09, IAPRS, vol. 38, pp. 134–138. Paris, France (2009)

  111. Salima, O., Taleb-Ahmed, A., Mohamed, B.: Spatial information based image clustering with a swarm approach. IAES Int. J. Artif. Intell. (IJ-AI) 1(3), 149–160 (2012)

    Article  Google Scholar 

  112. Sathya, D.J., Geetha, K.: Mass classification in breast DCE-MR images using an artificial neural network trained via a bee colony optimization algorithm. ScienceAsia 39(3), 294–305 (2013)

    Article  Google Scholar 

  113. Schiezaro, M., Pedrini, H.: Data feature selection based on artificial bee colony algorithm. EURASIP J. Image Video Process. 2013(47), 1–8 (2013)

    Google Scholar 

  114. Schwefel, H.P.: Kybernetische Evolution als Strategie der exprimentellen Forschung in der Strmungstechnik. Master’s thesis, Technical University of Berlin (1965)

  115. Seyman, M., Taspinar, N.: Pilot tones optimization using artificial bee colony algorithm for MIMO–OFDM systems. Wirel. Pers. Commun. 71(1), 151–163 (2013)

    Article  MathSciNet  Google Scholar 

  116. Shanthi, S., Bhaskaran, V.M.: Modified artificial bee colony based feature selection: a new method in the application of mammogram image classification. Int. J. Sci. Eng. Technol. Res. 3(6), 1664–1667 (2014)

    Google Scholar 

  117. Sharma, P., Bhavya, V., Navyashree, K., Sunil, K.S., Pavithra, P.: Artificial bee colony and its application for image fusion. Int. J. Inf. Technol. Comput. Sci. 4(11), 42–49 (2012)

    Google Scholar 

  118. Sharo, A., Raimond, K.: Enhancing degraded color images using fuzzy logic and artificial bee colony. Int. J. Comput. Eng. Res. 3(3), 356–361 (2013)

    Google Scholar 

  119. Shibai, Y., Xiangmo, Z., Weixing, W., et al.: A fuzzy partition entropy approach for multi-thresholding segmentation based on the recursive artificial bee colony algorithm. J. Xian Jiaotong Univ. 46(10), 72–77 (2012)

    MATH  Google Scholar 

  120. Shokouhifar, M., Abkenar, G.S.: An artificial bee colony optimization for mri fuzzy segmentation of brain tissue. In: International Conference on Management and Artificial Intelligence. IPEDR, vol. 6, pp. 6–10. IACSIT Press, Bali, Indonesia (2011)

  121. Sivaramakrishnan, A., Karnan, M.: Medical image segmentation using firefly algorithm and enhanced bee colony optimization. In: International Conference on Information and Image Processing (ICIIP-2014). Bonfring, pp. 316–321 (2014)

  122. Soni, V., Bhandari, A., Kumar, A., Singh, G.: Improved sub-band adaptive thresholding function for denoising of satellite image based on evolutionary algorithms. IET Signal Process. 7(8), 720–730 (2013)

    Article  Google Scholar 

  123. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  124. Sun, X., Yang, L., Zhang, B., Gao, L., Zhang, L.: Hyperspectral image clustering method based on artificial bee colony algorithm. In: 2013 Sixth International Conference on Advanced Computational Intelligence (ICACI), pp. 106–109 (2013)

  125. Taherdangkoo, M.: Skull removal in MR images using a modified artificial bee colony optimization algorithm. Technol. Health Care 22(5), 775–784 (2014)

    Google Scholar 

  126. Taspinar, N., Karaboga, D., Yildirim, M., Akay, B.: Papr reduction using artificial bee colony algorithm in OFDM systems. Turk. J. Electr. Eng. Comput. Sci. 19(1), 47–58 (2011)

    Google Scholar 

  127. Taspinar, N., Karaboga, D., Yildirim, M., Akay, B.: Partial transmit sequences based on artificial bee colony algorithm for PAPR reduction in MC-CDMA systems. IET Commun. 5(8), 1155–1162 (2011)

    Article  Google Scholar 

  128. Thiagarajan, B., Bremananth, R.: Brain image segmentation using conditional random field based on modified artificial bee colony optimization algorithm. Recent Adv. Comput. Sci., 93–106 (2014)

  129. Tsai, P.W., Khan, M.K., Pan, J.S., Liao, B.Y.: Interactive artificial bee colony supported passive continuous authentication system. IEEE Syst. J. 8(2), 395–405 (2014)

    Article  Google Scholar 

  130. Lichman, M.: UCI Machine Learning Repository. University of California, School of Information and Computer Sciences, Irvine (2013). http://archive.ics.uci.edu/ml

  131. Ustun, D., Ozdemir, C., Akdagli, A., Toktas, A., Bicer, M.B.: A powerful method based on artificial bee colony algorithm for translational motion compensation of ISAR image. Microw. Opt. Technol. Lett. 56(11), 2691–2698 (2014)

    Article  Google Scholar 

  132. Wang, S.: Artificial bee colony used for rigid image registration. Int. J. Res. Rev. Soft Intell. Comput. 1(2), 33–36 (2011)

    Article  Google Scholar 

  133. Wang, Z., Liu, X., Zhang, J.: Performance evaluation in color-based image retrieval using artificial bee colony algorithm. J. Inf. Comput. Sci. 11(4), 1077–1086 (2014)

    Article  Google Scholar 

  134. Xiao, Y., Cao, Y., Yu, W., Tian, J.: Multi-level threshold selection based on artificial bee colony algorithm and maximum entropy for image segmentation. Int. J. Comput. Appl. Technol. 43(4), 343–350 (2012)

    Article  Google Scholar 

  135. Xu, C., Duan, H.: Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft. Pattern Recognit. Lett. 31(13), 1759–1772 (2010). meta-heuristic Intelligence Based Image Processing

    Article  Google Scholar 

  136. Yan, Y., Liu, P., Zhang, Y., Su, N., Tian, S., Gao, F., Shen, Y.: A stereo remote sensing feature selection method based on artificial bee colony algorithm. In: Huang, B., Chang, C.I., López, J.F. (eds.) SPIE 9124, Satellite Data Compression, Communications, and Processing X, vol. 9124. SPIE-Intl Soc Optical Eng (2014)

  137. Yang, C.Z., Zheng, X.S.: Based on artificial bee colony video feature classification of video watermarking algorithm. In: Yarlagadda, P., Kim, Y. (eds.) Conference: International Conference on Mechatronics and Industrial Informatics (ICMII 2013), Applied Mechanics and Materials, vol. 321–324, pp. 1186–1190. Trans Tech Publications. doi:10.4028/www.scientific.net/amm.321-324.1186 (2013)

  138. Ye, Z., Hu, Z., Lai, X.: Image segmentation using thresholding and swarm intelligence. J. Softw. 7(5), 1074–1082 (2012)

    Article  Google Scholar 

  139. Yigitbasi, E., Baykan, N.: Edge detection using artificial bee colony algorithm (ABC). Int. J. Inf. Electron. Eng. 3(6), 634–638 (2013)

    Google Scholar 

  140. Yilmaz, B., Ozbay, Y.: Contrast enhancement using linear image combinations algorithm (ceulica) for enhancing brain magnetic resonance images. Turk. J. Electr. Eng. Comput. Sci. 22, 1540–1563 (2014)

    Article  Google Scholar 

  141. Yimit, A., Hagihara, Y., Miyoshi, T., Hagihara, Y.: Automatic image enhancement by artificial bee colony algorithm. In: Zhu, Z. (ed.) International Conference on Graphic and Image Processing (ICGIP 2012). Proceedings of SPIE, vol. 8768 (2013), 4th International Conference on Graphic and Image Processing (ICGIP), Singapore, Singapore, (OCT 06–07 2012)

  142. Yu, J., Duan, H.: Artificial bee colony approach to information granulation-based fuzzy radial basis function neural networks for image fusion. Opt. Int. J. Light Electron Opt. 124(17), 3103–3111 (2013)

    Article  Google Scholar 

  143. Yun-Fei, C., Wei-Yua, Y., Yong-Hao, X., Yong-Chang, C., Jiu-Chao, F.: Image segmentation using artificial bee colony and fast fuzzy c-means algorithms. Adv. Sci. Lett. 6(1), 841–844 (2012)

  144. Zhang, X., Duan, H., Gao, X.: Attitude parameters extraction of UAV based on hybrid computer vision and improved artificial bee colony algorithm. In: 2013 32nd Chinese Control Conference (CCC), pp. 3887–3890 (2013)

  145. Zhang, Z., Lin, J., Shi, Y.: Joint angle-frequency estimation based on WSF using artificial bee colony algorithm. In: 2013 International Conference on Information Science and Technology (ICIST), pp. 1312–1315 (2013)

  146. Zhang, Y., Lu, K., Gao, Y., Yang, B.: Analysis of image thresholding segmentation algorithms based on swarm intelligence. In: Wang, Y., Tan, L., Zhou, J. (eds.) SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, SPIE-Intl Soc Optical Eng. doi:10.1117/12.2010732 (2013)

  147. Zhang, Y., Wu, L., Wang, S.: Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Prog. Electromagn. Res. PIER 116, 65–79 (2011)

    Article  Google Scholar 

  148. Zhang, Y., Wu, L.: Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach. Entropy 13(4), 841–859 (2011)

    Article  MATH  Google Scholar 

  149. Zhang, Z., Lin, J., Shi, Y., Sun, X.: Joint direction-of-arrival and doppler frequency estimation based on artificial bee colony algorithm. J. Jilin Univ. Eng. Technol. Edit. 43(4), 1104–1109 (2013)

    Google Scholar 

  150. Zhang, Q., Duan, H.: Biological weight selection of multi-scale retinex via artificial bee colony algorithm. Opt. Int. J. Light Electron Opt. 125(3), 1434–1438 (2014)

    Article  Google Scholar 

  151. Zhang, Y., Tian, X., Ren, P.: An adaptive bilateral filter based framework for image denoising. Neurocomputing 140, 299–316 (2014)

    Article  Google Scholar 

  152. Zhao, D., Gao, H., Diao, M., An, C.: Direction finding of maximum likelihood algorithm using artificial bee colony in the impulsive noise. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI), vol. 2, pp. 102–105 (2010)

  153. Zhao, Z., Yin, D., Jiang, Y.: Improved bee colony algorithm based on knowledge strategy for digital filter design. Int. J. Comput. Appl. Technol. 47(2–3), 241–248 (2013)

    Article  Google Scholar 

  154. Zhiwei, Y., Mengdi, Z., Zhengbing, H., Hongwei, C.: image enhancement based on artificial bee colony algorithm and fuzzy set. In: Povloviq, C.B., Lu, C.W. (eds.) 3rd International Symposium on Information Engineering and Electronic Commerce (IEEC 2011), Proceedings. pp. 127–130. Huangshi Inst Technol; Res Assoc Modern Educ & Comp Sci; Huazhong Univ Sci & Technol; Ternopil Natl Econ Univ; Harbin Univ Technol; Wuhan Univ (2011), 3rd International Symposium on Information Engineering and Electronic Commerce (IEEC 2011), Huangshi, Peoples Republic of China, July 22–24, 2011

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  1. Department of Computer Engineering, Erciyes University, 38039, Melikgazi, Kayseri, Turkey

    Bahriye Akay & Dervis Karaboga

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Akay, B., Karaboga, D. A survey on the applications of artificial bee colony in signal, image, and video processing. SIViP 9, 967–990 (2015). https://doi.org/10.1007/s11760-015-0758-4

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