文章基本信息

标题：Gaussian Kernelized Fuzzy c-means with Spatial Information Algorithm for Image Segmentation
本地全文：下载
作者：Liu, Cuiyin ; Zhang, Xiuqiong ; Li, Xiaofeng 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2012
卷号：7
期号：6
页码：1511-1518
DOI：10.4304/jcp.7.6.1511-1518
语种：English
出版社：Academy Publisher
摘要：FCM is used for image segmentation in some applications. It is based on a specific distance norm and does not use spatial information of the image, so it has some drawbacks. Various kinds of improvements have been developed to extend the adaptability, such as BFCM, SFCM and KFCM. These methods extend FCM from two aspects, one is replacing the Euclidean norm, and the other is considering the spatial information constraints for clustering. Kernel distance can improve the robustness for multi-distribution data sets. Spatial information can help eliminate the sensitivity to noises and outliers. In this paper, Gaussian kernel-based fuzzy c-means algorithm with spatial information (KSFCM) is proposed. KSFCM is more robust and adaptive. The experiment results showd that KSFCM has the better performance.
关键词：kernel;spatial information;segmentation;fcm