Skip to main content
Springer Nature Link
Log in

Robust Dynamic Semi-supervised Picture Fuzzy Clustering with KL Divergence and Local Information

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Robust image segmentation is a research hot point in recent years, and the segmentation of images corrupted by high noise is a challenging topic in this field. Picture fuzzy clustering is a novel potent computation intelligence method for pattern analysis and machine intelligence. Motivated by these, this paper aims to present a robust dynamic semi-supervised picture fuzzy clustering algorithm with spatial information constraints to meet the need for segmenting images with high noise. To explore the study, this paper firstly constructs a weighted square Euclidean distance by combining the current pixel with its neighborhood spatial information to measure the difference between the current pixel and the clustering center. Secondly, inspired by the idea of semi-supervised clustering, the weighted local membership information of the current pixel is embedded into the picture fuzzy clustering through KL divergence, and a novel dynamic semi-supervised fuzzy clustering is obtained. Thirdly, for further enhancement of the anti-noise ability of semi-supervised fuzzy clustering, a picture fuzzy local information factor is constructed by fusing picture fuzzy partition information with weighted square Euclidean distance and introduced into dynamic semi-supervised fuzzy clustering to form a robust picture fuzzy clustering-related algorithm for image segmentation in the presence of high noise. The experiments on a series of synthetic images, medical images, remote sensing images, and standard datasets illustrate how our proposed algorithm works. This proposed algorithm has excellent segmentation performance and anti-noise robustness and outperforms eight state-of-the-art fuzzy or picture fuzzy clustering-related algorithms on four standard datasets in the presence of high noise.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bai X, Zhang Y, Liu H, Chen Z. Similarity measure-based possibilistic FCM with label information for brain MRI segmentation. IEEE Trans Cybern. 2019;49(7):2618–26.

    Article  Google Scholar 

  2. Carata S, Neagoe V. A pulse-coupled neural network approach for image segmentation and its pattern recognition application. Int Conf Commun (COMM). 2016;2016:61–4.

    Google Scholar 

  3. Luo J, Wang Y, Wang QH, Zhai RF, Zong YH. Automatic image segmentation of grape based on computer vision. Int Conf Intell Interact Syst Appl. 2017;541:365–70.

    Google Scholar 

  4. Wan L, Zhang T, Xiang Y, You H. A robust fuzzy C-means algorithm based on Bayesian nonlocal spatial information for SAR image segmentation. IEEE J Sel Top Appl Earth Observ Remote Sen. 2018;11(3):896–906.

    Article  Google Scholar 

  5. Beevi SZ, Sathik MM, Senthamaraikannan K. A robust fuzzy clustering technique with spatial neighborhood information for effective medical image segmentation. Int J Comput Sci Inform Secur. 2010;7(3):1–8.

    Google Scholar 

  6. Hong L, Jain A. Integrating faces and fingerprints for personal identification. IEEE Trans Pattern Anal Mach Intell. 1998;20(12):1295–307.

    Article  Google Scholar 

  7. Bezdek JC, Hathaway RJ, Sabin MJ, Tucker W. Convergence theory for fuzzy c-means: Counterexamples and repairs. IEEE Trans Syst Man Cybern. 1987;17(5):873–7.

    Article  MATH  Google Scholar 

  8. Ahmed MN, Yamany SM, Mohamed N. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging. 2002;21(3):193–9.

    Article  Google Scholar 

  9. Chen S, Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern Part B (Cybernetics). 2004;8(4):1907–16.

  10. Zhang DQ, Chen SC. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med. 2004;32(1):37–50.

    Article  Google Scholar 

  11. Krinidis S, Chatzis V. A robust fuzzy local information C-means clustering algorithm. IEEE Trans Image Process. 2010;19(5):1328–37.

    Article  MathSciNet  MATH  Google Scholar 

  12. Hou L. Study on image segmentation based on spatial information constraint clustering algorithm. East China Normal University. 2016.

  13. Gong M, Zhou Z, Ma J. Changes detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans Image Process. 2011;21(4):2141–51.

    Article  MathSciNet  MATH  Google Scholar 

  14. Tang Y, Ren F, Pedrycz W. Fuzzy c-means clustering through SSIM and patch for image segmentation. Appl Soft Comput. 2020;87:1–16.

    Article  Google Scholar 

  15. Ichihashi H, Miyagishi K, Honda K. Fuzzy c-means clustering with regularization by K-L Information. IEEE Int Conf Fuzzy Syst. 2001;924–27.

  16. Ngo LT, Mai DS, Pedrycz W. Semi-supervising interval type-2 fuzzy c-means clustering with spatial information for multi-spectral satellite image classification and change detection. Comput Geosci. 2015;83:1–16.

    Article  Google Scholar 

  17. Tuan TM, Ngan TT, Son LH. A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental X-ray image segmentation. Appl Intell. 2016;45:402–28.

    Article  Google Scholar 

  18. Son LH, Tuan TM. A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation. Expert Syst Appl. 2016;46:380–93.

    Article  Google Scholar 

  19. Son LH, Tuan TM. Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints. Eng Appl Artif Intell. 2017;59:186–95.

    Article  Google Scholar 

  20. Hayat AD, Ahmed AA. MR brain image segmentation based on unsupervised and semi-supervised fuzzy clustering methods. Int Conf Digit Image Comput Tech Appl (DICTA). 2016;1–7.

  21. Wu CM, Shangguan RY. Semi-supervised neutrosophic clustering algorithm based on HMRF. J Huazhong Univ Sci Technol. 2017;45(8):52–7.

    Google Scholar 

  22. Chaira T. A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images. Appl Soft Comput. 2011;11(2):1711–7.

    Article  Google Scholar 

  23. Son LH. DPFCM: A novel distributed picture fuzzy clustering method on picture fuzzy sets. Expert Syst Appl. 2015;42(1):51–66.

    Article  Google Scholar 

  24. Thong PH, Son LH. Picture fuzzy clustering: a new computational intelligence method. Soft Comput. 2016;20(9):3549–62.

    Article  MATH  Google Scholar 

  25. Wu CM, Wu QP. A robust picture segmentation algorithm based on improved PFCM. J Xi’an Univ Posts Telecommun. 2017;22(5):37–43.

    Google Scholar 

  26. Wu CM, Sun JM. Regularized picture fuzzy clustering and its robust segmentation algorithm. Comput Eng Appl. 2019;55(11):179–86.

    Google Scholar 

  27. Wu CM, Chen Y. Adaptive entropy weighted picture fuzzy clustering algorithm with spatial information for image segmentation. Appl Soft Comput. 2020;86:1–23.

    Article  Google Scholar 

  28. Gharieb RR, Gendy G. Fuzzy C-means with a local membership KL distance for medical image segmentation. Cairo Int Biomed Eng Conf (CIBEC). 2014;47–50.

  29. Gharieb RR, Gendy G. Fuzzy c-means with a local membership KL distance for image Segmentation. J Pattern Recognit Res. 2015;1:53–60.

    Article  Google Scholar 

  30. Gong M, Su L, Meng J, Chen W. Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans Fuzzy Syst. 2014;22(1):98–109.

    Article  Google Scholar 

  31. Zhao QH, Jia SH, Gao J, Gao X. Fuzzy clustering with spatial constrained membership for image segmentation. Sci Surveying Mapp. 2019;44(5):164–70.

    Google Scholar 

  32. Wu CM, Kang ZQ. Robust entropy-based symmetric regularized picture fuzzy clustering for image segmentation. Digit Signal Process. 2021;110:1–29.

    Article  Google Scholar 

  33. Gong MM, Liang Y, Shi J, Ma W, Ma J. Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process. 2013;22(2):573–84.

    Article  MathSciNet  MATH  Google Scholar 

  34. Xiang DL, Tang T, Hu CB, Su Y. A kernel clustering algorithm with fuzzy factor: application to SAR image segmentation. IEEE Geosci Remote Sens Lett. 2014;11(7):1290–4.

    Article  Google Scholar 

  35. Zhao F, Liu HQ, Fan JL. Multi-objective evolutionary clustering image segmentation based on complementary spatial information. J Electron Inf Technol. 2015;37(3):672–8.

    Google Scholar 

  36. Zhao QH, Jia SH, Gao J, Gao X. Fuzzy clustering image segmentation combined with membership space constraints. Sci Surveying Mapp. 2019;44(5):164–70.

    Google Scholar 

  37. Saha A, Das S. Stronger convergence results for the center-based fuzzy clustering with convex divergence measure. IEEE Trans Cybern. 2019;49(12):4229–42.

    Article  Google Scholar 

  38. Giordana N, Pieczynski W. Estimation of generalized multisensor hidden Markov chains and unsupervised image segmentation. IEEE Trans Pattern Anal Mach Intell. 1997;19(5):465–75.

    Article  Google Scholar 

  39. Sanjith S, Ganesan R, Isaac RSR. Experimental analysis of compacted satellite image quality using different compression methods. Adv Sci Eng Med. 2015;7(3):227–33.

    Article  Google Scholar 

  40. Gharieb RR, Gendy G, Selim H. A hard c-means clustering algorithm incorporating membership KL Divergence and local data information for noisy image segmentation. Int J Pattern Recognit Artif Intell. 2018;32(4):758–69.

    Article  Google Scholar 

  41. Thong PH, Son LH. A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality. Knowl Based Syst. 2016;109:48–60.

    Article  Google Scholar 

  42. Maulik U, Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell. 2002;24(12):1650–4.

    Article  Google Scholar 

  43. Gan H, Fan Y, Luo Z, Zhang Q. Local homogeneous consistent safe semi-supervised clustering. Expert Syst Appl. 2017;97:384–93.

    Article  Google Scholar 

  44. Gan H. Safe semi-supervised fuzzy c-means clustering. IEEE Access. 2019;7:95659–64.

    Article  Google Scholar 

  45. Yang Y, Wu C, Li Y, Zhang S. Robust semi-supervised kernelized fuzzy local information c-means clustering for image segmentation. Math Probl Eng. 2020;2020(3):1–22.

    Google Scholar 

  46. Fan J, Zhen W, Xie W. Suppressed fuzzy C-means clustering algorithm. Pattern Recogn Lett. 2003;24(9):1607–12.

    Article  MATH  Google Scholar 

  47. Zhao F, Fan J, Liu H. Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation. Expert Syst Appl. 2014;41(9):4083–93.

    Article  Google Scholar 

  48. Wu C, Liu N. Suppressed robust picture fuzzy clustering for image segmentation. Soft Comput. 2021;25(3):1–24.

    MathSciNet  Google Scholar 

  49. Bharill N, Tiwari A, Malviya A. Fuzzy based scalable clustering algorithms for handling big data using apache spark. IEEE Trans Big Data. 2016;2(4):339–52.

  50. Preeti J, Arunai T, Neha B, Mukkamalla M, Neha N. Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis. Comput Biol Chem. 2021;92:107454.

  51. Anderson D, Luke R, Keller JM. Speedup of fuzzy clustering through stream processing on graphics processing units. IEEE Trans Fuzzy Syst. 2008;16(4):1101–6.

    Article  Google Scholar 

  52. Ait Ali N, Cherradi B, EI Abbassi A, Omar B, Mohamed Y. GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation. Multimed Tools Appl. 2018;77:21221–43.

  53. Zhang J, Cao Y, Wu Q. Vector of locally and adaptively aggregated descriptors for image feature representation. Pattern Recognit. 2021;116:107952. To be published, https://doi.org/10.1016/j.patcog.2021.107952.

  54. Yu J, Tan M, Zhang H, Rui Y, Tao D. Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell. 2019. To be published, https://doi.org/10.1109/TPAMI.2019.2932058.

  55. Seo J, Park H. Object recognition in very low resolution images using deep collaborative learning. IEEE Access. 2019;7:134071–82.

    Article  Google Scholar 

  56. Ren L, Lu J, Feng J, Zhou J. Uniform and variational deep Learning for RGB-D object recognition and person re-identification. IEEE Trans Image Process. 2019;28(10):4970–83.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive suggestions to improve the overall quality of the paper. Besides, the authors would like to thank the School of Electronic Engineering, Xi’an University of Posts & Telecommunications, Xi’an, China, for the financial support.

Funding

This work was supported by the National Natural Science Foundation of China (61671377,51709228) and the Shaanxi Natural Science Foundation of China (2016JM8034, 2017JM6107).

Author information

Authors and Affiliations

  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, People’s Republic of China

    Chengmao Wu, Jiajia Zhang, Congcong Huang & Xiaokang Guo

Authors
  1. Chengmao Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jiajia Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Congcong Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xiaokang Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jiajia Zhang.

Ethics declarations

Ethics Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Conflict of Interest

The authors declare no competing interests.

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

Wu, C., Zhang, J., Huang, C. et al. Robust Dynamic Semi-supervised Picture Fuzzy Clustering with KL Divergence and Local Information. Cogn Comput 14, 970–988 (2022). https://doi.org/10.1007/s12559-021-09988-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-021-09988-6

Keywords