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KSLIC: K-mediods Clustering Based Simple Linear Iterative Clustering

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Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11858))

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Abstract

Simple Linear Iterative Clustering (SLIC) is one of the most excellent superpixel segmentation algorithms with the most comprehensive performance and is widely used in various scenes of production and living. As a preprocessing step in image processing, superpixel segmentation should meet various demands in real life as much as possible, but SLIC is highly sensitive to noise. In this paper, a K-mediods clustering based simple linear iterative clustering (KSLIC) is proposed, which replaces the K-means clustering in SLIC with a modified local K-mediods clustering. To evaluate the performance of KSLIC, we test it on BSD500 benchmark dataset. The results show that it outperforms SLIC in terms of different noise environments including Gaussian noise, multiplicative noise and salt and pepper noise.

This work was jointly supported by the Natural Science Foundation of Hubei Province with Grant No. 2016CFB481, the National Natural Science Foundation of China with Grant No. 61703375, and the 111 project with Grant No. B17040.

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References

  1. https://ivrl.epfl.ch/research-2/research-current/research-superpixels/

  2. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. EPFL Technical report 149300 (2010)

    Google Scholar 

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

    Article  Google Scholar 

  4. Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 670–677 (2009)

    Google Scholar 

  5. 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. (2018). https://doi.org/10.1109/TFUZZ.2018.2889018

    Article  Google Scholar 

  6. Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.: Turbopixels: fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)

    Article  Google Scholar 

  7. Li, S., Huang, J., Shang, J., Wei, X.: A robust simple linear iterative clustering algorithm. In: IEEE International Conference on Signal and Image Processing (2017)

    Google Scholar 

  8. Mori, G.: Guiding model search using segmentation. In: Tenth IEEE International Conference on Computer Vision, 2, pp. 1417–1423 (2005)

    Google Scholar 

  9. Pólya, G.: Mathematics and Plausible Reasoning: Induction and Analogy in Mathematics, vol. 1. Princeton University Press, Princeton (1990)

    MATH  Google Scholar 

  10. Radhakrishna, A., Appu, S., Kevin, S., Aurelien, L., Pascal, F., Sabine, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  11. Ren, M.: Learning a classification model for segmentation. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 10–17 (2003)

    Google Scholar 

  12. Schick, A., Fischer, M., Stiefelhagen, R.: An evaluation of the compactness of superpixels. Pattern Recogn. Lett. 43(1), 71–80 (2014)

    Article  Google Scholar 

  13. Wang, S., Lu, H., Yang, F., Yang, M.: Superpixel tracking. In: 2011 International Conference on Computer Vision, pp. 1323–1330 (2011)

    Google Scholar 

  14. Sun, W., Liao, Q., Xue, J.H., Zhou, F.: SPSIM: a superpixel-based similarity index for full-reference image quality assessment. IEEE Trans. Image Process. 27(9), 4232–4244 (2018)

    Article  MathSciNet  Google Scholar 

  15. Veksler, O., Boykov, Y., Mehrani, P.: Superpixels and supervoxels in an energy optimization framework. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 211–224. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15555-0_16

    Chapter  Google Scholar 

  16. Wang, C., Liu, Z., Chan, S.C.: Superpixel-based hand gesture recognition with kinect depth camera. IEEE Trans. Multimed. 17(1), 29–39 (2014)

    Article  Google Scholar 

  17. Wu, C., Zhang, L., Zhang, H., Yan, H.: Improved superpixel-based fast fuzzy C-means clustering for image segmentation. In: IEEE International Conference on Image Processing (ICIP) (2019)

    Google Scholar 

  18. Wu, C., Zhang, L., Zhang, H., Yan, H.: Fuzzy SLIC: fuzzy simple linear iterative clustering. arXiv preprint arXiv:1812.10932 (2018)

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Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Houwang Zhang & Yuan Zhu

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China

    Houwang Zhang & Yuan Zhu

Authors
  1. Houwang Zhang
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  2. Yuan Zhu
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Corresponding author

Correspondence to Houwang Zhang .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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Zhang, H., Zhu, Y. (2019). KSLIC: K-mediods Clustering Based Simple Linear Iterative Clustering. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_44

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  • DOI: https://doi.org/10.1007/978-3-030-31723-2_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31722-5

  • Online ISBN: 978-3-030-31723-2

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