针对传统模糊C均值聚类方法对噪声敏感和过分依赖于初始聚类中心的缺点，提出基于SSCL的模糊C均值图像分类的自适应算法。该算法首先通过SSCL获得初始类别数和类别中心，然后作为模糊C均值聚类的输入，自动对图像进行分割，并对图像分割结果利用空间信息进行后处理。实验结果表明该方法较好地解决了FCM算法中的初始化和噪声敏感问题，具有较好的分类结果。

An adaptive fuzzy C-means image classification algorithm based on SSCL is proposed, in order to overcome the shortcomings that traditional fuzzy C-means clustering algorithm is noise-sensitive and relies excessively on initial cluster centers. First we obtain the cluster centers using SSCL, then treat the cluster centers as the initial value of fuzzy C-means, so an adaptive image classification can be achieved. At last, post processing is implemented using space information. Experiment results show that proposed algorithm is less sensitive to noise and initial cluster centers in FCM method, and has better classification accuracy.