Residual-driven Fuzzy C-Means Clustering for Image Segmentation
文献类型：期刊论文
| 作者 | Cong Wang ; Witold Pedrycz; ZhiWu Li; MengChu Zhou
|
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2021 |
| 卷号 | 8期号:4页码:876-889 |
| ISSN号 | 2329-9266 |
| 关键词 | Fuzzy C-Means image segmentation mixed or unknown noise residual-driven weighted regularization |
| DOI | 10.1109/JAS.2020.1003420 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43954]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Cong Wang,Witold Pedrycz,ZhiWu Li,et al. Residual-driven Fuzzy C-Means Clustering for Image Segmentation[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(4):876-889. |
| APA | Cong Wang,Witold Pedrycz,ZhiWu Li,&MengChu Zhou.(2021).Residual-driven Fuzzy C-Means Clustering for Image Segmentation.IEEE/CAA Journal of Automatica Sinica,8(4),876-889. |
| MLA | Cong Wang,et al."Residual-driven Fuzzy C-Means Clustering for Image Segmentation".IEEE/CAA Journal of Automatica Sinica 8.4(2021):876-889. |
入库方式： OAI收割
来源：自动化研究所
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