摘要
针对传统FCM算法处理噪声图像时存在去噪性能差、聚类时间长、分割效果不佳等问题。文中通过拟合核聚类算法和传统的FCM算法,产生一种使用内核诱导距离取代欧式距离的核函数FCM算法,并推导出利用样本特征和空间信息的核FCM聚类算法,通过大量的对比测试,得出文中算法较传统FCM算法在图像的分割和去噪时间上减少约68%,峰值信噪比相比传统FCM算法提高了约10%。证明优化后的算法具有更好的抗噪性与鲁棒性。
When Traditional FCM algorithm processes the noisy images, it exists the poor performance of image denoising, slow clustering, and poor segmentation performance. The nuclear clustering algorithm is combined with the traditional FCM algorithm using the kernel induced distance instead of the new clustering method of Euclidean distance, a modified kernel-based FCM clustering algorithm is deduced by using the spatial information and features of samples. Comparative tests show that this algorithm reduces the time of image segmentation and de-noising by 68% with an improved PSNR by about 10% compared with the traditional FCM algorithm, exhibiting better noise resistance and robustness.
出处
《电子科技》
2016年第2期16-19,共4页
Electronic Science and Technology
关键词
FCM
内核诱导距离
核聚类
鲁棒性
FCM
kernel induced distance
kernel clustering
robustness
