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Volume 8 Issue 4
Apr.  2021

IEEE/CAA Journal of Automatica Sinica

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Cong Wang, Witold Pedrycz, ZhiWu Li and MengChu Zhou, "Residual-driven Fuzzy C-Means Clustering for Image Segmentation," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 876-889, Apr. 2021. doi: 10.1109/JAS.2020.1003420
Citation: Cong Wang, Witold Pedrycz, ZhiWu Li and MengChu Zhou, "Residual-driven Fuzzy C-Means Clustering for Image Segmentation," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 876-889, Apr. 2021. doi: 10.1109/JAS.2020.1003420

Residual-driven Fuzzy C-Means Clustering for Image Segmentation

doi: 10.1109/JAS.2020.1003420
Funds:  This work was supported in part by the Doctoral Students’ Short Term Study Abroad Scholarship Fund of Xidian University, the National Natural Science Foundation of China (61873342, 61672400, 62076189) , the Recruitment Program of Global Experts, and the Science and Technology Development Fund, MSAR (0012/2019/A1)
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  • In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and enables noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise. Built on this framework, a weighted $ \ell_{2}$-norm regularization term is presented by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.

     

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  • 1http://www.bic.mni.mcgill.ca/brainweb/
    2https: //www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html
    3http://research.microsoft.com/vision/cambridge/recognition/
    4http://neo.sci.gsfc.nasa.gov/
    5http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html
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    Highlights

    • For the first time, we propose an RFCM framework for image segmentation.
    • WRFCM is presented by weighting mixed noise distribution and incorporating spatial information.
    • We design a two-step iterative algorithm to realize WRFCM.
    • WRFCM is validated to produce state-of-the-art performance.

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