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Kernel-Distance-Based Intuitionistic Fuzzy c-Means Clustering Algorithm and Its Application

  • MATHEMATICAL THEORY OF PATTERN RECOGNITION
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Abstract

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

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REFERENCES

  1. E. H. Ruspini, “A new approach to clustering,” Inf. Control 15 (1), 22–32 (1969).

    Article  Google Scholar 

  2. J. W. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed. (Morgan Kaufmann, San Francisco, 2012).

    MATH  Google Scholar 

  3. J. C. Bezdek, R. Ehrlich, and W. Full, “FCM: The fuzzy c-means clustering algorithm,” Comput. Geosci. 10 (2), 191–203 (1984).

    Article  Google Scholar 

  4. X. Lei and H. Ouyang, “Image segmentation algorithm based on improved fuzzy clustering,” Cluster Comput. 1–11 (2018). https://doi.org/10.1007/s10586-018-2128-9

    Article  Google Scholar 

  5. L.-M. Tang, H.-K. Wang, Z.-H. Chen, and D.‑R. Huang, “Image fuzzy clustering segmentation based on variational level set,” J. Softw. 25 (7), 1570–1582 (2014) [in Chinese].

    MATH  Google Scholar 

  6. T. Chaira and A. Ray, “Fuzzy measures for color image retrieval,” Fuzzy Sets Sys. 150 (3), 545–560 (2005).

    Article  MathSciNet  Google Scholar 

  7. R. Lan and J. Ma, “Image segmentation based on intuitionstic fuzzy C-means clustering algorithm,” J. Xi’an Univ. Posts Telecom. 21 (4), 53–56 (2016) [in Chinese].

    Google Scholar 

  8. T. Chaira, “A rank ordered filter for medical image edge enhancement and detection using intuitionistic fuzzy set,” Appl. Soft Comput. 12 (4), 1259–1266 (2012).

    Article  Google Scholar 

  9. T. Chaira, “A novel intuitionistic fuzzy c-means clustering algorithm and its application to medical images,” Appl. Soft Comput. 11 (2), 1711–1717 (2011).

    Article  Google Scholar 

  10. X.-D. Yu, Y.-J. Lei, S.-H. Yue, and R. Wang, “Research on PSO-based intuitionistic fuzzy kernel clustering algorithm,” J. Commun. 36 (5), 74–80 (2015) [in Chinese].

    Google Scholar 

  11. J.-Y. Kang, Z. Ji, and C. Gong, “Kernelized fuzzy c‑means clustering algorithm and its application,” Chinese J. Sci. Instr. 31 (7), 1657–1663 (2010) [in Chinese].

    Google Scholar 

  12. B. Ma, H.-Y. Qu, and H.-S. Wong, “Kernel clustering-based discriminant analysis,” Pattern Recognit. 40 (1), 324–327 (2007).

    Article  Google Scholar 

  13. M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. Image Process. 22 (2), 573–584 (2013).

    Article  MathSciNet  Google Scholar 

  14. J. Zhang and Z. Li, “Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy c‑means,” J. Electron. Inf. Technol. 39 (9), 2162–2168 (2017) [in Chinese].

    Google Scholar 

  15. K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets Sys. 20 (1), 87–96 (1986).

    Article  Google Scholar 

  16. X.-B. Gao, J.-H. Pei, W.-X. Xie, “A study of weighting exponent m in a fuzzy c-means algorithm,” Acta Electron. Sin. 28 (4), 80–83 (2000) [in Chinese].

    Google Scholar 

  17. Z. Xu, J. Chen, and J. Wu, “Clustering algorithm for intuitionistic fuzzy sets,” Inf. Sci. 178 (19), 3775–3790 (2008).

    Article  MathSciNet  Google Scholar 

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Funding

This research was funded by National Natural Science Foundation of China grant no. 61573299.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, 410082, Hunan, Changsha, China

    Lei Xiangxiao & Ouyang Honglin

  2. Changsha Social Work College, 410004, Hunan, Changsha, China

    Lei Xiangxiao & Xu Lijuan

Authors
  1. Lei Xiangxiao
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  2. Ouyang Honglin
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  3. Xu Lijuan
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Corresponding author

Correspondence to Xu Lijuan.

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The authors declare that they have no conflicts of interest.

Additional information

Xiangxiao Lei. Doctor of Engineering, Associate Professor. Graduated from the Central South University in 2005. Worked in Changsha social Work College. He research interests include intelligent control and digital image processing.

Honglin Ouyang. Doctor of Engineering, Professor. Graduated from the Hunan University in 2005. Worked in Hunan university. He research interests include Power electronics and control engineering field.

Lijuan Xu. Associate Professor. Graduated from the Central South University in 2005. Worked in Changsha social Work College. She research interests include intelligent control.

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Xiangxiao, L., Honglin, O. & Lijuan, X. Kernel-Distance-Based Intuitionistic Fuzzy c-Means Clustering Algorithm and Its Application. Pattern Recognit. Image Anal. 29, 592–597 (2019). https://doi.org/10.1134/S1054661819040199

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  • DOI: https://doi.org/10.1134/S1054661819040199

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