摘要
提出了基于粗糙集模糊聚类与差分免疫克隆聚类的图像分割算法.该算法在差分免疫克隆聚类算法的基础上,通过引入粗糙集模糊聚类,将差分免疫克隆聚类算法中的硬聚类变成模糊聚类,从而获得更丰富的聚类信息.具体来说,由于粗糙集的优势是处理不确定的数据,因此,加入粗糙集模糊聚类后更有利于算法解决不确定性问题.通过对9幅图像分割实验结果与4种算法的对比,验证了该算法在聚类性能稳定性方面的优越性,结果还同时证明了该算法具有更高的分割正确率和更好的分割结果.
In this paper, a new method based on rough-fuzzy set and differential immune clone clustering algorithm （DICCA） for image segmentation is proposed. By replacing hard clustering with fuzzy clustering through incorporating rough-fuzzy set into DICCA, this algorithm can obtain more abundant clustering information. Specially, as the advantage of rough set is processing uncertain data, the proposed algorithm is more conducive to solve the uncertainty problem. In experiments, nine images are used for segmentation and four algorithms are chosen for comparison to validate the performance in the clustering stability. The experimental results show that the algorithm has higher segmentation accuracy and better segmentation results.
出处
《软件学报》
EI
CSCD
北大核心
2014年第11期2675-2689,共15页
Journal of Software
基金
国家自然科学基金(61203303
61202176
61272279)
关键词
粗糙集
差分免疫克隆
图像分割
rough set
differential immune clone
image segmentation
