%0 Journal Article
%T Natural image segmentation algorithm with unsupervised FCM<br>无监督模糊C均值聚类自然图像分割算法
%A JI ZEXUAN
%A CHEN QIANG
%A SUN QUANSEN
%A XIA DESHEN
%A <br>纪则轩
%A 陈强
%A 孙权森
%A 夏德深
%J 中国图象图形学报
%D 2011
%I 
%X In this work, we propose a natural image segmentation method based on unsupervised fuzzy C-means (USFCM) clustering algorithm. The intersection of confidence intervals rules is utilized to adaptively compute the scale of Gabor filter for each pixel. Then image features are measured by Gabor filter with adaptively computed scale, orientation, frequency and phase. Meanwhile, a fast polynomial segmentation method is proposed to determine the number of clusters. Then the algorithm USFCM is utilized to get the final segmentation. The experimental results show that the proposed method can overcome the impact of texture and distinguish the target from background. The performances have demonstrated the effectiveness, accuracy and superiority of the proposed method.
%K nature image segmentation
%K unsupervised clustering
%K FCM
%K Gabor filter
%K intersection of confidence intervals (ICI)
%K texture features<br>自然图像分割
%K 无监督聚类
%K 模糊C均值
%K Gabor滤波
%K 置信区间交集
%K 纹理特征
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=009FB9768EC44ADB1CCCC8C9F3C633F7&yid=9377ED8094509821&vid=7801E6FC5AE9020C&iid=94C357A881DFC066&sid=1F7317C17A9AF4FA&eid=839A12D3ACF8C715&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=29