ABSTRACT Aim to that Neutrosophic C-mean clustering segmentation does not consider the membership distribution of every sample point to different classes. Herein, an image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering is proposed. When the maximum membership value of a sample point is far greater than other membership values, the centre of the class with the maximum membership value is taken as the centre of the fuzzy class. Otherwise, the average value of the centre of the two classes with the highest and second-highest membership values is used as the centre of the fuzzy class. In the preprocessing stage, wavelet technology is used to remove noise from the processed image, and the improved Bayesian algorithm is employed to calculate the filter threshold. The experiment results for synthetic and natural images show that the proposed method is more accurate and effective than the existing methods.

中文翻译：

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基于小波和数据驱动的中智模糊聚类的图像分割算法

摘要的目标是中智 C 均值聚类分割不考虑每个样本点对不同类别的隶属度分布。在此，提出了一种基于小波和数据驱动的中智模糊聚类的图像分割算法。当样本点的最大隶属度值远大于其他隶属度值时，以隶属度最大的类的中心作为模糊类的中心。否则，将具有最高和次高隶属度值的两个类的中心的平均值作为模糊类的中心。在预处理阶段，利用小波技术去除处理图像中的噪声，采用改进的贝叶斯算法计算滤波阈值。