【摘要】 基于模糊C均值聚类(FCM)的图像分割是应用较为广泛的方法之一,其具有描述简洁、易于实现、分割效果好等优点,但也存在运算时间过长等问题.本文提出了一种新的快速FCM图像分割算法,该算法首先将图像数据划分成一定数量的子集,然后利用区域粗糙度标记所有子集,最后根据子集质心及其权重进行模糊聚类图像分割.仿真实验结果表明,该算法能够以保证图像分割质量为前提,大幅度提高FCM图像分割速度,故具有一定应用价值.更多还原

【Abstract】 Fuzzy c-means (FCM) clustering is one of well-known unsupervised clustering techniques,which has been widely used in automated image segmentation. However,when the classical FCM algorithm is used for image segmentation,there are also some problems,such as the heavy calculating burden. In this paper,we present an efficient algorithm to implement a FCM clustering that produces clusters comparable to slower methods. In our algorithm,we partition the original image dataset into unit blocks,mark the blocks by area quantization and cluster the centroids of the unit blocks. In this way,we can dramatically reduce the time for calculating final converged centroids. Experiments show that this algorithm produces comparable clustering results as FCM algorithm,but has much faster computation speed than classical FCM algorithm.更多还原