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

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 proposes a multi-objective  evolutionary fuzzy clustering algorithm for image segmentation based on semi-supervision. The proposed technique simultaneously optimizes the semi-supervised fuzzy compactness and fuzzy separation among the clusters 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 measure 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.

Key words: multi-objective evolutionary algorithm, image segmentation, semi-supervision, fuzzy clustering, similarity measure