耦合先验拉普拉斯坐标的半监督图像分割算法

针对传统半监督图像分割方法难以精确分割分散或细小区域的缺陷，提出了一种耦合标签先验和拉普拉斯坐标模型的半监督图像分割算法。首先，扩展拉普拉斯坐标（LC）模型，通过引入标签先验项进一步精确表征未标记像素点与已标记像素点之间的关系。然后，基于矩阵方程的求导优化，有效估计像素属于标签的后验概率，以实现图像目标分割的任务。得益于标签先验的引入，所提算法对分散或细小区域的分割更加鲁棒。最后，在多个公开的半监督分割数据集上实验结果表明，相比拉普拉斯坐标算法，所提算法的分割准确率获得了显著提升，验证了所提算法的有效性。

Semi-supervised image segmentation based on prior Laplacian coordinates

Focusing on the issue that classic semi-supervised image segmentation methods have difficulty in accurately segmenting scattered or small regions, a semi-supervised segmentation algorithm based on label prior and Laplacian Coordinates (LC) was proposed. Firstly, the Laplacian coordinates model was extended, and further the relationship between unlabeled pixels and labeled pixels accurately characterized by introducing the label prior. Secondly, based on the derivation of matrix equation, the posterior probability that the pixel belongs to the label was able to be effectively estimated, thus achieving the segmentation of the image. Thanks to the introduction of the label prior, the algorithm was more robust to the segmentation of scattered and small regions. Lastly, the experimental results on several public semi-supervised segmentation datasets show that the segmentation accuracy of the proposed algorithm is significantly improved compared with that of the Laplacian coordinates algorithm, which verifies the effectiveness of the proposed algorithm.