文献类型：会议论文

英文题名：Maximum Fuzzy Entropy and Immune Clone Selection Algorithm for Image Segmentation

作者：Tian, WenJie[1];Geng, Yu[1];Liu, JiCheng[1];Ai, Lan[1]

通讯作者：Tian, WJ[1]

机构：[1]Beijing Union Univ, Automat Inst, Beijing, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), Beijing Union Univ, Automat Inst, Beijing, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：Asia-Pacific Conference on Information Processing (APCIP 2009)

会议日期：JUL 14-19, 2009

会议地点：Shenzhen, PEOPLES R CHINA

语种：英文

外文关键词：maximum fuzzy entropy; image segmentation; immune clone selection algorithm; membership function; optimal parameter

摘要：This paper is concerned with fuzzy entropy definition used for image segmentation. The key problem associated with this method is to find the optimal parameter combination of membership function so that an image can be transformed into fuzzy domain with maximum fuzzy entropy. An improved immune clone selection algorithm (ICSA) is proposed to search the optimal parameter combination. Then, we compare the proposed ICSA with other artificial intelligence models. The experiment indicates that the proposed method is quite effective and ubiquitous.