针对现有半监督模糊C均值聚类(FCM)算法无法充分利用半监督信息和参数选择的问题，提出了一种利用监督信息细化类簇和自适应参数选择的半监督FCM图像分割算法。通过对监督信息进行预聚类来细化类簇从而确定最佳聚类数；此外，利用图像像素与监督像素的颜色差距进行标签传递，使得监督信息可以充分指导聚类过程；最后，依据标签像素的空间信息实现监督项参数的自适应选择，并结合CIE Lab颜色系统完成图像的分割。在不同数据集上的实验结果表明，该算法能较为有效地分割复杂彩色图像，在分割准确度和平均交并比(mIoU)方面优于其他几种FCM算法，在Berkeley数据集上平均分割准确度和平均交并比分别达到了96.40%和89.66%。

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.