Abstract: To address the issues of existing semi-supervised fuzzy C-means (FCM) clustering algorithms, which fail to fully utilize semi-supervised information and have difficulties in parameter selection, this paper proposes a semi-supervised FCM image segmentation algorithm that leverages supervised information to refine clusters and employs adaptive parameter selection. By pre-clustering the supervised information to refine the clusters, the algorithm determines the optimal number of clusters. Additionally, it utilizes the color differences between image pixels and supervised pixels for label propagation, enabling the supervised information to fully guide the clustering process. Finally, the algorithm achieves adaptive selection of supervision term parameters based on the spatial information of labeled pixels and completes image segmentation using the CIE Lab color system. Experimental results on various datasets demonstrate that this algorithm can effectively segment complex color images, outperform- ing several other FCM algorithms in terms of segmentation accuracy and mean intersection over union (mIoU). On the Berkeley dataset, the average segmentation accuracy and mean IoU reach 96.40% and 89.66%, respectively.

Key words: image segmentation, fuzzy C-means clustering, semi-supervised clustering, label propagation, density peaks clustering