中文摘要：

针对传统模糊C均值（FCM）聚类算法对结构复杂图像分割效果不理想且算法执行效率较低的缺陷,提出一种融合均值平移（mean shift）的FCM聚类算法.利用mean shift算法将图像分成若干同质区域,将此区域视为新的节点;通过图像局部信息熵描述新节点的空间和灰度特征;采用能较好模拟人眼非线性视觉响应的指数函数进行相似性测度.实验结果表明,对于复杂背景图像和含噪声图像,所提出的算法在目标提取效果和执行效率上具有较强的鲁棒性.

英文摘要：

An improved FCM combining mean shift algorithm is proposed to improve the segmentation visual effects and efficiency of traditional FCM. Firstly, image is segmented into many small homogeneous regions by using mean shift pre-segmentation algorithm, and the homogeneous regions, instead of pixels are taken as new nodes. Then, image local entropy is adopted to describe the new nodes spatial and gray feature. Finally, an exponential function which can simulate well the human nonlinear visual response is used to measure the similarity between new nodes and cluster center nodes. Experimental results on both complex background and noises images show that the proposed algorithm has better robustness to the segmentation effects and efficiency.