基于ELM的局部空间信息的模糊C均值聚类图像分割算法

极限学习机（Extreme learning machine, ELM）作为一种新技术具有在回归和分类中良好的泛化性能。局部空间信息的模糊C均值算法(Weighted fuzzy local information C-means, WFLICM)用邻域像素点的空间信息标记中心点的影响因子，增强了模糊C均值聚类算法的去噪声能力。基于极限学习机理论，对WFLICM进行改进优化，提出了基于ELM的局部空间信息的模糊C均值聚类图像分割算法(New kernel weighted fuzzy local information C-means based on ELM,ELM-NKWFLICM)。该方法基于ELM特征映射技术，将原始数据通过ELM特征映射技术映射到高维ELM隐空间中，再用改进的新核局部空间信息的模糊C均值聚类图像分割算法(New kernel weighted fuzzy local information C-means,NKWFLICM)进行聚类。 实验结果表明 ELM-NKWFLICM算法具有比WFLICM算法更强的去噪声能力，且很好地保留了原图像的细节，算法在处理复杂非线性数据时更高效, 同时克服了模糊聚类算法对模糊指数的敏感性问题。

Fuzzy C-Means Clustering Image Segmentation Algorithm with Local Spatial Information Based on ELM

As a new technology, extreme learning machine (ELM) has good generalization performance in regression and classification. Weighted fuzzy local information C-means (WFLICM) uses point coordinate distance and the local pixel coefficient of variation to mark the impact factor of each point to the middle point, improving the robustness of fuzzy C-means cluster algorithm. Based on ELM and improving WFLICM, new kernel weighted fuzzy local information C-means based on ELM (ELM-NKWFLICM) is proposed. The method is based on ELM feature mapping technique, mapping the original data to the high-dimensional ELM hidden space through the ELM feature mapping technique, and then is clustered by the new kernel weighted fuzzy local information c-means (NKWFLICM) of the improved new kernel local spatial information. Experimental results show that the proposed algorithm has better robustness than the WFLICM algorithm and retains the original image details well. The algorithm is more efficient in dealing with complex nonlinear data, and overcomes the sensitivity of fuzzy clustering algorithm to fuzzy exponents.