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Fast Fuzzy c-Means Clustering Algorithm with Spatial Constraints for Image Segmentation

机译：具有空间约束的快速模糊c均值聚类算法用于图像分割

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摘要

Fuzzy c-means clustering (FCM) with spatial constraints (FCM-S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to the introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. The contextual information can raise its insensitivity to noise to some extent. Although the robustness of the FCM-S algorithm is better, the convergence speed of it is lower. In this paper, to overcome the problem that FCM-S algorithm is time consuming, a fast fuzzy c-means clustering algorithm with spatial constraints (FFCM-S) is proposed. To speed up FCM-S calculations, FFCM-S algorithm modified the degree of memberships. Experiments on the artificial and real-world datasets show that our proposed algorithm is more effective.
机译：具有空间约束的模糊c均值聚类（FCM-S）是一种适用于图像分割的有效算法。它的有效性不仅有助于为每个像素的归属引入模糊性，而且还有助于开发空间上下文信息。上下文信息可以在某种程度上提高其对噪声的不敏感性。尽管FCM-S算法的鲁棒性较好，但收敛速度较低。为了克服FCM-S算法耗时的问题，提出了一种具有空间约束的快速模糊c均值聚类算法（FFCM-S）。为了加快FCM-S的计算速度，FFCM-S算法修改了隶属度。在人工和真实数据集上的实验表明，我们提出的算法更有效。

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