Skip to main content
SpringerLink
Log in

Image segmentation using fuzzy competitive learning based counter propagation network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Abdel-Khalek S et al (2017) A two-dimensional image segmentation method based on genetic algorithm and entropy. Optik. 131:414–422. https://doi.org/10.1016/j.ijleo.2016.11.039

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.asoc.2017.01.003

    Article  Google Scholar 

  3. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans PAMI 33(5):898–916

    Article  Google Scholar 

  4. Arumugadevi S, Seenivasagam V (2015) Comparison of clustering methods for segmenting color images. Indian J Sci Technol 8(7):670–677

    Article  Google Scholar 

  5. Arumugadevi S, Seenivasagam V (2016) Color image segmentation using feedforward neural networks with FCM. Int J Autom Comput 13(5):491–500. https://doi.org/10.1007/s11633-016-0975-5

    Article  Google Scholar 

  6. Bdiri T, Bouguila N (2013) Bayesian learning of inverted Dirichlet mixtures for SVM kernels generation. Neural Comput Applic 23(5):1443–1458

    Article  Google Scholar 

  7. Bhattacharyya S et al (2010) Multilevel image segmentation with adaptive image context based thresholding. Appl Soft Comput 11:946–962. https://doi.org/10.1016/j.asoc.2010.01.015

    Article  Google Scholar 

  8. Borges VR et al (2015) An iterative fuzzy region competition algorithm for multiphase image segmentation. Soft Comput 19:339–351. https://doi.org/10.1007/s00500-014-1256-2l

    Article  Google Scholar 

  9. Chouhan SS, Kaul A, Singh UP (2018) Image segmentation using computational intelligence techniques: review. Arch Comput Meth Eng. https://doi.org/10.1007/s11831-018-9257-4

    Article  MathSciNet  Google Scholar 

  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. https://doi.org/10.1016/j.patcog.2017.03.009

    Article  Google Scholar 

  11. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans PAMI 24(5):1–18

    Article  Google Scholar 

  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):996–1002

    Article  Google Scholar 

  13. De S et al (2012) Color image segmentation using parallel OptiMUSIG activation function. Appl Soft Comput 12:3228–3236. https://doi.org/10.1016/j.asoc.2012.05.011

    Article  Google Scholar 

  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):762–774

    Article  Google Scholar 

  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, Heidelberg

    Google Scholar 

  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. https://doi.org/10.1007/s11760-016-0992-4

    Article  Google Scholar 

  17. Helmy AK, El-Taweel GS (2016) Image segmentation scheme based on SOM–PCNN in frequency domain. Appl Soft Comput 40:405–415. https://doi.org/10.1016/j.asoc.2015.11.042

    Article  Google Scholar 

  18. Huang Y, Long Y (2006) Super-resolution using neural networks based on the optimal recovery theory. J Comput Electron 5(4):275–281

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.neucom.2016.03.046

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.asoc.2015.03.029

    Article  Google Scholar 

  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. https://doi.org/10.1016/j.asoc.2015.12.040

    Article  Google Scholar 

  22. Li Y, Shen Y (2014) An automatic fuzzy c-means algorithm for image segmentation. Soft Comput 14:123–128. https://doi.org/10.1007/s00500-009-0442-0

    Article  Google Scholar 

  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. https://doi.org/10.1109/ACCESS.2016.2613940

    Article  Google Scholar 

  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). https://doi.org/10.1109/VSMM.2010.5665969.

  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 2576-2582

  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 1266-1272

  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)

  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 419-424

  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):44–53

    Article  Google Scholar 

  30. Maszczyk T, Duch W (2008) Comparison of Shannon, Renyi and Tsallis entropy used in decision trees. Lect Notes Comput Sci 5097:643–651

    Article  Google Scholar 

  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 Photonics

  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. https://doi.org/10.1016/j.asoc.2016.05.026

    Article  Google Scholar 

  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):3436–3439

    Google Scholar 

  34. Opbroek AV, vander Lijn F, de Bruijne M et al (2013) Automated brain-tissue segmentation by multi-feature SVM classification, Bigr. Nl, 2013

  35. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67

    Article  MathSciNet  Google Scholar 

  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. https://doi.org/10.1007/s40815-016-0145-5

    Article  MathSciNet  Google Scholar 

  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, Cham

    Chapter  Google Scholar 

  38. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans PAMI 22(8):888–905

    Article  Google Scholar 

  39. Shibai Y, Yiming Q, Gong M (2017) Unsupervised hierarchical image segmentation through fuzzy entropy maximization. Pattern Recogn 68:245–269. https://doi.org/10.1016/j.patcog.2017.03.012

    Article  Google Scholar 

  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. https://doi.org/10.1142/S1469026816500164

    Article  Google Scholar 

  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. https://doi.org/10.1007/s00500-017-2522-x

    Article  Google Scholar 

  42. Singh UP, Jain S, Tiwari A, Singh RK (2018) Gradient evolution-based counter propagation network for approximation of noncanonical system. Soft Comput. https://doi.org/10.1007/s00500-018-3160-7

    Article  Google Scholar 

  43. Wang Z, Ma Y, Cheng F, Yang L (2010) Review of pulse-coupled neural networks. Image Vis Comput 28(1):5–13

    Article  Google Scholar 

  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 108:160–172

    Google Scholar 

  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):1382–1389

    Article  Google Scholar 

  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. https://doi.org/10.1007/s11704-015-4543-x

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science & Engineering, Shri Mata Vaishno Devi University, Katra, 182320, India

    Siddharth Singh Chouhan & Ajay Kaul

  2. School of Mathematics, Shri Mata Vaishno Devi University, Katra, 182320, India

    Uday Pratap Singh

Authors
  1. Siddharth Singh Chouhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ajay Kaul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Uday Pratap Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siddharth Singh Chouhan.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chouhan, S.S., Kaul, A. & Singh, U.P. Image segmentation using fuzzy competitive learning based counter propagation network. Multimed Tools Appl 78, 35263–35287 (2019). https://doi.org/10.1007/s11042-019-08094-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08094-y

Keywords

Search

Navigation