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Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering
IEEE Transactions on Fuzzy Systems ( IF 11.9 ) Pub Date : 2018-10-01 , DOI: 10.1109/tfuzz.2018.2796074
Tao Lei , Xiaohong Jia , Yanning Zhang , Lifeng He , Hongying Meng , Asoke K. Nandi

As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation. However, the introduction of local spatial information often leads to a high computational complexity, arising out of an iterative calculation of the distance between pixels within local spatial neighbors and clustering centers. To address this issue, an improved FCM algorithm based on morphological reconstruction and membership filtering (FRFCM) that is significantly faster and more robust than FCM is proposed in this paper. First, the local spatial information of images is incorporated into FRFCM by introducing morphological reconstruction operation to guarantee noise-immunity and image detail-preservation. Second, the modification of membership partition, based on the distance between pixels within local spatial neighbors and clustering centers, is replaced by local membership filtering that depends only on the spatial neighbors of membership partition. Compared with state-of-the-art algorithms, the proposed FRFCM algorithm is simpler and significantly faster, since it is unnecessary to compute the distance between pixels within local spatial neighbors and clustering centers. In addition, it is efficient for noisy image segmentation because membership filtering are able to improve membership partition matrix efficiently. Experiments performed on synthetic and real-world images demonstrate that the proposed algorithm not only achieves better results, but also requires less time than the state-of-the-art algorithms for image segmentation.

中文翻译:

基于形态重建和成员过滤的快速鲁棒模糊C均值聚类算法

由于模糊 c 均值聚类 (FCM) 算法对噪声敏感,因此通常将局部空间信息引入目标函数以提高 FCM 算法对图像分割的鲁棒性。然而,由于局部空间邻居和聚类中心内像素之间的距离的迭代计算,局部空间信息的引入通常会导致高计算复杂度。为了解决这个问题,本文提出了一种基于形态重建和隶属过滤(FRFCM)的改进 FCM 算法,该算法比 FCM 更快、更鲁棒。首先,通过引入形态重建操作将图像的局部空间信息纳入 FRFCM,以保证抗噪和图像细节保留。第二,基于局部空间邻居和聚类中心内像素之间的距离的隶属划分的修改被仅依赖于隶属划分的空间邻居的局部隶属过滤所取代。与最先进的算法相比,所提出的 FRFCM 算法更简单且速度更快,因为无需计算局部空间邻居和聚类中心内的像素之间的距离。此外,由于隶属过滤能够有效地改进隶属划分矩阵,因此它对于噪声图像分割是有效的。在合成和真实世界图像上进行的实验表明,所提出的算法不仅取得了更好的结果,而且比最先进的图像分割算法所需的时间更少。基于局部空间邻居和聚类中心内像素之间的距离,由仅依赖于隶属划分的空间邻居的局部隶属过滤代替。与最先进的算法相比,所提出的 FRFCM 算法更简单且速度更快,因为无需计算局部空间邻居和聚类中心内的像素之间的距离。此外,由于隶属过滤能够有效地改进隶属划分矩阵,因此它对于噪声图像分割是有效的。在合成和真实世界图像上进行的实验表明,所提出的算法不仅取得了更好的结果,而且比最先进的图像分割算法所需的时间更少。基于局部空间邻居和聚类中心内像素之间的距离,由仅依赖于隶属划分的空间邻居的局部隶属过滤代替。与最先进的算法相比,所提出的 FRFCM 算法更简单且速度更快,因为无需计算局部空间邻居和聚类中心内的像素之间的距离。此外,由于隶属过滤能够有效地改进隶属划分矩阵,因此它对于噪声图像分割是有效的。在合成和真实世界图像上进行的实验表明,所提出的算法不仅取得了更好的结果,而且比最先进的图像分割算法所需的时间更少。
更新日期:2018-10-01
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