计算机工程与应用2017，Vol.53Issue(22)：40-44,76,6.DOI:10.3778/j.issn.1002-8331.1607-0015
基于半监督的多目标进化模糊聚类算法
Multi-objective evolutionary fuzzy clustering algorithm based on semi-supervision
摘要
Abstract
In order to solve the traditional clustering image segmentation results not well because of the lack of effective guidance, it introduces a semi-supervised approach as a multi-objective evolutionary fuzzy clustering algorithm, and pro-poses a multi-objective evolutionary fuzzy clustering algorithm for image segmentation based on semi-supervision. The pro-posed technique simultaneously optimizes the semi-supervised fuzzy compactness and fuzzy separation among the clus-ters and makes use of monitoring information to guide the clustering process. In the final generation, it produces a set of non-dominated solutions, from which the best solution in terms of a proposed validity index BI based on similarity mea-sure is chosen to be the best clustering solution. Experimental results show that compared with other unsupervised fuzzy algorithms, the proposed clustering technique can effectively improve the clustering accuracy and the segmentation result in vision.关键词
多目标进化算法/图像分割/半监督/模糊聚类/相似性度量Key words
multi-objective evolutionary algorithm/image segmentation/semi-supervision/fuzzy clustering/similarity measure分类
信息技术与安全科学引用本文复制引用
王俊,赵凤..基于半监督的多目标进化模糊聚类算法[J].计算机工程与应用,2017,53(22):40-44,76,6.基金项目
国家自然科学基金(No.61571361,No.61102095,No.61202153,No.61340040) （No.61571361,No.61102095,No.61202153,No.61340040）
陕西省科技计划项目(No.2014KJXX-72). （No.2014KJXX-72）
