Multimedia Tools and Applications

, Volume 78, Issue 24, pp 35263–35287 | Cite as

Image segmentation using fuzzy competitive learning based counter propagation network

  • Siddharth Singh ChouhanEmail author
  • Ajay Kaul
  • Uday Pratap Singh


Image segmentation is the method of partitioning an image into some homogenous regions that are more meaningful for its better understanding and examination. Soft computing methods having the capabilities of achieving artificial intelligence are predominately used to perform the task of segmentation. Due to the variability and the uncertainty present in natural scenes, segmentation is a complicated task to perform with the help of conventional image segmentation techniques. Therefore, in this article a hybrid Fuzzy Competitive Learning based Counter Propagation Network (FCPN) is proposed for the segmentation of natural scene images. This method compromises of the uncertainty handling capabilities of the fuzzy system and proficiency of parallel learning ability of neural network. To identify the number of clusters automatically in less computational time, the instar layer of Counter propagation network (CPN) has been trained by using Fuzzy competitive learning (FCL). The outstar layer of counter propagation network is trained by using Grossberg learning for obtaining the desired output. Region growing method having the tendency to correctly identify edges with simplicity is used for initial seed point selection. Then, the most similar regions in the image are clustered and the number of clusters is estimated automatically. Finally, by identifying the cluster centers the images are segmented. Bacterial foraging algorithm is used to initialize the initial weights to the network, which helps the proposed method in achieving low convergence ratio with higher accuracy. Results validated the higher performance of proposed FCPN method when compared with other states-of-the-art methods. For future work, some other adaptive methods like the fuzzy model-based network can be used to identify multiple object regions and classifying them among separate clusters.


Bacterial foraging algorithm Counter propagation network Fuzzy competitive learning Image segmentation Soft computing 



  1. 1.
    Abdel-Khalek S et al (2017) A two-dimensional image segmentation method based on genetic algorithm and entropy. Optik. 131:414–422. CrossRefGoogle Scholar
  2. 2.
    Aghajari E, Chandrashekhar GD (2017) Self-organizing map based extended fuzzy C-means (SEEFC) algorithm for image segmentation. Appl Soft Comput 54:347–363. CrossRefGoogle Scholar
  3. 3.
    Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans PAMI 33(5):898–916CrossRefGoogle Scholar
  4. 4.
    Arumugadevi S, Seenivasagam V (2015) Comparison of clustering methods for segmenting color images. Indian J Sci Technol 8(7):670–677CrossRefGoogle Scholar
  5. 5.
    Arumugadevi S, Seenivasagam V (2016) Color image segmentation using feedforward neural networks with FCM. Int J Autom Comput 13(5):491–500. CrossRefGoogle Scholar
  6. 6.
    Bdiri T, Bouguila N (2013) Bayesian learning of inverted Dirichlet mixtures for SVM kernels generation. Neural Comput Applic 23(5):1443–1458CrossRefGoogle Scholar
  7. 7.
    Bhattacharyya S et al (2010) Multilevel image segmentation with adaptive image context based thresholding. Appl Soft Comput 11:946–962. CrossRefGoogle Scholar
  8. 8.
    Borges VR et al (2015) An iterative fuzzy region competition algorithm for multiphase image segmentation. Soft Comput 19:339–351. CrossRefGoogle Scholar
  9. 9.
    Chouhan SS, Kaul A, Singh UP (2018) Image segmentation using computational intelligence techniques: review. Arch Comput Meth Eng. MathSciNetCrossRefGoogle Scholar
  10. 10.
    Choy SK, Shu YL, Yu KW et al (2017) Fuzzy model-based clustering and its application in image segmentation. Pattern Recogn 68:141–157. CrossRefGoogle Scholar
  11. 11.
    Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans PAMI 24(5):1–18CrossRefGoogle Scholar
  12. 12.
    Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low and high dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002CrossRefGoogle Scholar
  13. 13.
    De S et al (2012) Color image segmentation using parallel OptiMUSIG activation function. Appl Soft Comput 12:3228–3236. CrossRefGoogle Scholar
  14. 14.
    Fan W, Bouguila N, Ziou D (2012) Variational learning for finite Dirichlet mixture models and applications. IEEE Trans Neural Netw Learn Syst 23(5):762–774CrossRefGoogle Scholar
  15. 15.
    Fu Z, Wang L (2012) Color image segmentation using Gaussian mixture model and EM algorithm. In: Wang FL, Lei J, Lau RWH, Zhang J (eds) Multimedia and signal processing. Communications in Computer and Information Science, vol 346. Springer, Berlin, HeidelbergGoogle Scholar
  16. 16.
    Gharieb RR, Gendy G, Abdelfattah A (2017) C-means clustering fuzzified by two membership relative entropy functions approach incorporating local data information for noisy image segmentation. SIViP 11(3):541–548. CrossRefGoogle Scholar
  17. 17.
    Helmy AK, El-Taweel GS (2016) Image segmentation scheme based on SOM–PCNN in frequency domain. Appl Soft Comput 40:405–415. CrossRefGoogle Scholar
  18. 18.
    Huang Y, Long Y (2006) Super-resolution using neural networks based on the optimal recovery theory. J Comput Electron 5(4):275–281CrossRefGoogle Scholar
  19. 19.
    Jiang XL, Qiang W, Biao H et al (2016) Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints. Neurocomputing. 207:22–35. CrossRefGoogle Scholar
  20. 20.
    Khan A, Muhammad A (2015) Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation. Appl Soft Comput 32:300–310. CrossRefGoogle Scholar
  21. 21.
    Konar D et al (2016) A quantum bi-directional self-organizing neural network (QBDSONN) architecture for binary object extraction from a noisy perspective. Appl Soft Comput 46:731–752. CrossRefGoogle Scholar
  22. 22.
    Li Y, Shen Y (2014) An automatic fuzzy c-means algorithm for image segmentation. Soft Comput 14:123–128. CrossRefGoogle Scholar
  23. 23.
    Li L et al (2016) Fuzzy multilevel image thresholding based on modified discrete Grey wolf optimizer and local information aggregation. IEEE Access 4:6438–6450. CrossRefGoogle Scholar
  24. 24.
    Liu Y, Zhang X, Cui J, Wu C, Hamid A, Zha H (2010) Visual analysis of child-adult interactive behaviors in video sequences, 16th International Conference on Virtual Systems and Multimedia (VSMM).
  25. 25.
    Liu Y, Zheng Y, Liang Y, Liu S, Rosenblum DS (2016) Urban water quality prediction based on multi-task multi-view learning. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), p 2576-2582Google Scholar
  26. 26.
    Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), p 1266-1272Google Scholar
  27. 27.
    Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune teller: predicting your career path, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)Google Scholar
  28. 28.
    Long Y, Huang Y (2006) Image based source camera identification using demosaicking. In: Proceedings of IEEE 8th workshop on multimedia signal processing, Victoria, Canada, p 419-424Google Scholar
  29. 29.
    Makrogiannis S, Economou G, Fotopoulos S (2005) A region dissimilarity relation that combines feature-space and spatial information for color image segmentation. IEEE Trans Syst Man Cybern B 35(1):44–53CrossRefGoogle Scholar
  30. 30.
    Maszczyk T, Duch W (2008) Comparison of Shannon, Renyi and Tsallis entropy used in decision trees. Lect Notes Comput Sci 5097:643–651CrossRefGoogle Scholar
  31. 31.
    Moeskops P, Viergever MA, Benders MJNL, Isgum I (2015) Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images. In: SPIE Medical Imaging, International Society for Optics and PhotonicsGoogle Scholar
  32. 32.
    Mondal A, Ghosh S, Ghosh A (2016) Robust global and local fuzzy energy based active contour for image segmentation. Appl Soft Comput 47(C):191–215. CrossRefGoogle Scholar
  33. 33.
    Nandagopalan S, Adiga BS, Deepak N (2008) A universal model for content-based image retrieval. International Journal of Computer and Information Engineering 2(10):3436–3439Google Scholar
  34. 34.
    Opbroek AV, vander Lijn F, de Bruijne M et al (2013) Automated brain-tissue segmentation by multi-feature SVM classification, Bigr. Nl, 2013Google Scholar
  35. 35.
    Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67MathSciNetCrossRefGoogle Scholar
  36. 36.
    Sakhre V, Singh UP, Jain S (2017) FCPN approach for uncertain nonlinear dynamical system with unknown disturbance. Int J Fuzzy Syst 19(2):452–469. MathSciNetCrossRefGoogle Scholar
  37. 37.
    Sayed A, Sardeshmukh M, Limkar S (2014) Optimisation Using Levenberg-Marquardt Algorithm of Neural Networks for Iris. In: Satapathy S, Udgata S, Biswal B (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol. 247. Springer, ChamCrossRefGoogle Scholar
  38. 38.
    Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans PAMI 22(8):888–905CrossRefGoogle Scholar
  39. 39.
    Shibai Y, Yiming Q, Gong M (2017) Unsupervised hierarchical image segmentation through fuzzy entropy maximization. Pattern Recogn 68:245–269. CrossRefGoogle Scholar
  40. 40.
    Singh UP, Jain S (2016) Modified chaotic bat algorithm-based counter propagation neural network for uncertain nonlinear discrete time system. Int J Comput Intell Appl 15(3):1650016. CrossRefGoogle Scholar
  41. 41.
    Singh UP, Jain S (2018) Optimization of neural network for nonlinear discrete time system using modified quaternion firefly algorithm: case study of Indian currency exchange rate prediction. Soft Comput 22(8):2667–2681. CrossRefGoogle Scholar
  42. 42.
    Singh UP, Jain S, Tiwari A, Singh RK (2018) Gradient evolution-based counter propagation network for approximation of noncanonical system. Soft Comput. CrossRefGoogle Scholar
  43. 43.
    Wang Z, Ma Y, Cheng F, Yang L (2010) Review of pulse-coupled neural networks. Image Vis Comput 28(1):5–13CrossRefGoogle Scholar
  44. 44.
    Wang L, Gao Y, Shi F, Li G et al (2015) Learning-based multi-source integration framework for segmentation of infant brain images. Neuro image 108:160–172Google Scholar
  45. 45.
    Wenbing T, Hai J, Yimin Z (2007) Color image segmentation based on mean shift and normalized cuts. IEEE Trans Syst Man Cybern B 37(5):1382–1389CrossRefGoogle Scholar
  46. 46.
    Zhao J, Ji G, Han X (2016) An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging. Front Comp Sci 10(1):189–200. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Siddharth Singh Chouhan
    • 1
    Email author
  • Ajay Kaul
    • 1
  • Uday Pratap Singh
    • 2
  1. 1.School of Computer Science & EngineeringShri Mata Vaishno Devi UniversityKatraIndia
  2. 2.School of MathematicsShri Mata Vaishno Devi UniversityKatraIndia

Personalised recommendations