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Accelerated intuitionistic fuzzy clustering for image segmentation

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Abstract

To improve processing time of the intuitionistic fuzzy C-means during color image segmentation, this paper proposes a scheme based on two clustering stages. In the first, a downsampled image is used to isolate the dominant color of the images by means of centroids calculating. Later, in the second stage these centroids are used during the image segmentation. With these two processes, an algorithmic acceleration of approximately eleven times can be guaranteed compared to the conventional algorithm. The effectiveness of this proposal is verified by experiments on the natural color images of datasets such as BSDS500 Alpert et al. Segmentation Evaluation Database, Sky dataset, Stony Bro- ok University Shadow and ISIC 2018. The quality of the segmentation was quantified using metrics and compared with other current methods of the state of the art. The results obtained show a superior performance of the proposed method both in segmentation and in processing time.

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References

  1. Alexandre, E.B.: Ift-slic: geração de superpixels com base em agrupamento iterativo linear simples e transformada imagem-floresta. Ph.D. thesis, Universidade de São Paulo (2017)

  2. Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2007)

  3. Atanassov, K.T.: Intuitionistic Fuzzy Logics. Springer (2017). https://doi.org/10.1007/978-3-319-48953-7

  4. Bhandari, A.K., Rahul, K.: A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization. Appl. Soft Comput. 81, 105515 (2019). https://doi.org/10.1016/j.asoc.2019.105515

    Article  Google Scholar 

  5. Chaira, T.: Medical Image Processing: Advanced Fuzzy Set Theoretic Techniques. CRC Press (2015)

  6. Chaira, T.: Fuzzy Set and Its Extension. Wiley (2019). https://doi.org/10.1002/9781119544203

  7. Chen, J., Zheng, H., Lin, X., Wu, Y., Su, M.: A novel image segmentation method based on fast density clustering algorithm. Eng. Appl. Artif. Intell. 73, 92–110 (2018). https://doi.org/10.1016/j.engappai.2018.04.023

    Article  Google Scholar 

  8. Chi-Wah Kok, W.S.T.: Digital Image Interpolation in MATLAB. Wiley (2019). https://www.ebook.de/de/product/35338292/chi_wah_kok_wing_shan_tam_digital_image_interpolation_in_matlab.html

  9. Codella, N., Rotemberg, V., Tschandl, P., Celebi, M.E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368 (2019)

  10. García-Lamont, F., Cervantes, J., López-Chau, A., Yee-Rendón, A.: Automatic computing of number of clusters for color image segmentation employing fuzzy c-means by extracting chromaticity features of colors. Pattern Anal. Appl. 23(1), 59–84 (2018). https://doi.org/10.1007/s10044-018-0729-9

    Article  MathSciNet  Google Scholar 

  11. He, L., Huang, S.: An efficient krill herd algorithm for color image multilevel thresholding segmentation problem. Appl. Soft Comput. 89, 106063 (2020). https://doi.org/10.1016/j.asoc.2020.106063

    Article  Google Scholar 

  12. Jia, X., Lei, T., Du, X., Liu, S., Meng, H., Nandi, A.K.: Robust self-sparse fuzzy clustering for image segmentation. IEEE Access 12, (2020). https://doi.org/10.1109/access.2020.3015270

  13. Lei, T., Jia, X., Zhang, Y., He, L., Meng, H., Nandi, A.K.: Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Trans. Fuzzy Syst. 26(5), 3027–3041 (2018). https://doi.org/10.1109/tfuzz.2018.2796074

    Article  Google Scholar 

  14. Lei, T., Jia, X., Zhang, Y., Liu, S., Meng, H., Nandi, A.K.: Superpixel-based fast fuzzy c-means clustering for color image segmentation. IEEE Trans. Fuzzy Syst. 27(9), 1753–1766 (2019). https://doi.org/10.1109/tfuzz.2018.2889018

    Article  Google Scholar 

  15. Lei, T., Liu, P., Jia, X., Zhang, X., Meng, H., Nandi, A.K.: Automatic fuzzy clustering framework for image segmentation. IEEE Trans. Fuzzy Syst. 28(9), 2078–2092 (2020). https://doi.org/10.1109/tfuzz.2019.2930030

    Article  Google Scholar 

  16. Liu, H., Zhao, F., Chaudhary, V.: Pareto-based interval type-2 fuzzy c-means with multi-scale JND color histogram for image segmentation. Digit. Signal Proc. 76, 75–83 (2018). https://doi.org/10.1016/j.dsp.2018.02.005

    Article  MathSciNet  Google Scholar 

  17. Mittal, H., Saraswat, M.: 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 (2018). https://doi.org/10.1016/j.engappai.2018.03.001

    Article  Google Scholar 

  18. Mújica-Vargas, D., Gallegos-Funes, F.J., Rosales-Silva, A.J.: A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recogn. Lett. 34(4), 400–413 (2013)

    Article  Google Scholar 

  19. Mújica-Vargas, D., Gallegos-Funes, F.J., Rosales-Silva, A.J., de Jesús Rubio, J.: Robust c-prototypes algorithms for color image segmentation. EURASIP J. Image Video Process. (2013). https://doi.org/10.1186/1687-5281-2013-63

    Article  Google Scholar 

  20. Mújica-Vargas, D.: Redescending intuitionistic fuzzy clustering to brain magnetic resonance image segmentation. J. Intell. Fuzzy Syst. 39, 1097–1108 (2020). https://doi.org/10.3233/JIFS-192005

    Article  Google Scholar 

  21. Set, B.S.D.: Benchmarks 500 (bsds500) (2011). http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html

  22. Vicente, T.F.Y., Hou, L., Yu, C.P., Hoai, M., Samaras, D.: Large-scale training of shadow detectors with noisily-annotated shadow examples. In: European Conference on Computer Vision, pp. 816–832. Springer (2016)

  23. Wang, T., Ji, Z., Sun, Q., Chen, Q., Ge, Q., Yang, J.: Diffusive likelihood for interactive image segmentation. Pattern Recogn. 79, 440–451 (2018). https://doi.org/10.1016/j.patcog.2018.02.023

    Article  Google Scholar 

  24. Xing, Z.: An improved emperor penguin optimization based multilevel thresholding for color image segmentation. Knowl.-Based Syst. 194, 105570 (2020). https://doi.org/10.1016/j.knosys.2020.105570

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to the editors and the reviewers for their valuable comments. The authors thank the CONACYT, TecNM/CENIDET for their support of this research through the project “Clasificador para detectar fibrilación auricular en señales electrocardiográficas utilizando una red recurrente profunda entrenada con momentos de tiempo-frecuencia.”

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Authors and Affiliations

  1. Tecnológico Nacional de México/CENIDET, Cuernavaca-Morelos, Mexico

    Dante Mújica-Vargas

  2. Sección de Estudios de Posgrado e Investigación, Esime, Azcapotzalco, Instituto Politécnico Nacional, Ciudad de México, Mexico

    José de Jesús Rubio

Authors
  1. Dante Mújica-Vargas
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  2. José de Jesús Rubio
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Correspondence to Dante Mújica-Vargas.

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Mújica-Vargas, D., Rubio, J.d.J. Accelerated intuitionistic fuzzy clustering for image segmentation. SIViP 15, 1845–1852 (2021). https://doi.org/10.1007/s11760-021-01934-1

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