Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation
Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation
复制标题
结合局部信息进行图像分割的快速、鲁棒的模糊 C 均值聚类算法
DOI：
10.1016/j.patcog.2006.07.011
复制
发表时间：
2007-03-01
影响因子：
8
通讯作者：
Zhang, Daoqiang
中科院分区：
文献类型：
--
作者：
Cai, Weiling;Chen, Songean;Zhang, Daoqiang
具有空间约束的模糊c均值（FCM）算法已被证明是有效的图像分割算法。然而，它们仍然存在以下缺点：(1)虽然在相应的目标函数中引入了局部空间信息，在一定程度上增强了它们对噪声的不敏感性，但它们对噪声和离群值的鲁棒性仍然不足，特别是在没有先验知识的情况下；(2)在它们的目标函数中，存在一个关键参数alpha，用于平衡对噪声的鲁棒性和保留图像细节的有效性，一般通过经验选择；(3)分割图像的时间取决于图像的大小，因此图像的大小越大，分割时间越长。本文将局部空间信息和灰度信息结合在一起，提出了一种新的快速鲁棒的FCM图像分割框架，即快速广义模糊c均值聚类算法。FGFCM在克服FCM_S缺点的同时，提高了聚类性能。此外，FGFCM不仅包含了快速FCM和增强FCM等许多现有算法作为特例，而且还可以衍生出其他新算法，如本文其余部分提出的FGFCM_S1和FGFCM_S2。FGFCM的主要特点是：(1)采用新的因子S-ij作为局部（空间和灰度）相似性度量，既保证了图像的抗噪性，又保证了图像的细节保持，同时去除了经经验调整的参数alpha；(2)快速聚类或分割图像，分割时间仅取决于灰度级的个数q，而不取决于图像的大小N(>> q)，因此其计算复杂度从O（NcI(1)）降低到O(qcI(2))，其中c为聚类个数，I-1和I-2（一般< I-1）分别为标准FCM和我们提出的快速分割方法中的迭代次数。在合成图像和真实图像上的实验表明，FGFCM算法是有效的。(c) 2006模式识别学会。Elsevier Ltd.出版。版权所有。
Fuzzy c-means (FCM) algorithms with spatial constraints (FCM_S) have been proven effective for image segmentation. However, they still have the following disadvantages: (1) although the introduction of local spatial information to the corresponding objective functions enhances their insensitiveness to noise to some extent, they still lack enough robustness to noise and outliers, especially in absence of prior knowledge of the noise; (2) in their objective functions, there exists a crucial parameter alpha used to balance between robustness to noise and effectiveness of preserving the details of the image, it is selected generally through experience; and (3) the time of segmenting an image is dependent on the image size, and hence the larger the size of the image, the more the segmentation time. In this paper, by incorporating local spatial and gray information together, a novel fast and robust FCM framework for image segmentation, i.e., fast generalized fuzzy c-means (FGFCM) clustering algorithms, is proposed. FGFCM can mitigate the disadvantages of FCM_S and at the same time enhances the clustering performance. Furthermore, FGFCM not only includes many existing algorithms, such as fast FCM and enhanced FCM as its special cases, but also can derive other new algorithms such as FGFCM_S1 and FGFCM_S2 proposed in the rest of this paper. The major characteristics of FGFCM are: (1) to use a new factor S-ij as a local (both spatial and gray) similarity measure aiming to guarantee both noise-immunity and detail-preserving for image, and meanwhile remove the empirically-adjusted parameter alpha; (2) fast clustering or segmenting image, the segmenting time is only dependent on the number of the gray-levels q rather than the size N(>> q) of the image, and consequently its computational complexity is reduced from O(NcI (1)) to O(qcI (2)), where c is the number of the clusters, I-1 and I-2(< I-1, generally) are the numbers of iterations, respectively, in the standard FCM and our proposed fast segmentation method. The experiments on the synthetic and real-world images show that FGFCM algorithm is effective and efficient. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.