Symmetric regularized picture fuzzy clustering is a new fuzzy clustering method, and it is difficult to choose its weighting fuzzy factor m and lacks certain robustness to noises or outliers. To this end, this paper proposes a robust entropy-based symmetric regularized picture fuzzy clustering with spatial information constraints for noisy image segmentation. The idea of maximum entropy fuzzy clustering is firstly introduced into symmetric regularized picture fuzzy clustering, and we can obtain an entropy-based symmetric regularized picture fuzzy clustering method to avoid selecting weighted fuzzy factors m. Meanwhile, it has clear physical meaning. Then, spatial neighborhood information of current clustering pixel is embedded into its clustering objective function, and a new robust fuzzy clustering algorithm for image segmentation is obtained to enhance the ability to suppress noise. In the end, the convergence of new robust clustering algorithm is proved by Zangwill theorem. Many experimental results show that the proposed algorithm can achieve higher segmentation performance than existing picture fuzzy clustering.

对称正则化图片模糊聚类是一种新的模糊聚类方法，难以选择其加权模糊因子m，并且对噪声或离群值缺乏一定的鲁棒性。为此，本文提出了一种基于鲁棒的，具有空间信息约束的基于熵的对称正则化图片模糊聚类，用于噪声图像的分割。首先将最大熵模糊聚类的思想引入对称正则化图片模糊聚类中，从而得到一种基于熵的对称正则化图片模糊聚类方法，从而避免选择加权模糊因子m。。同时，它具有明确的物理意义。然后，将当前聚类像素的空间邻域信息嵌入到其聚类目标函数中，并获得了一种新的鲁棒模糊聚类算法进行图像分割，以提高抑制噪声的能力。最后，通过Zangwill定理证明了新的鲁棒聚类算法的收敛性。实验结果表明，与现有的图像模糊聚类算法相比，该算法具有更高的分割性能。