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中文摘要：
提出一种半监督K均值聚类和带状区域增长的三维网格模型层次分割算法，包括显著性特征点提取、预分割和后分割3个阶段．该算法在多维标度法的基础上进行显著性特征点提取；利用半监督K均值聚类算法来对原始模型进行初步的粗分割，以提高算法的整体效率；根据预分割结果，利用离散高斯曲率逼近，以带状推进的区域增长法进行层次的后分割．与同类算法相比，文中算法得到的分割边界更有意义，具有较高的边缘准确性和分割区域一致性．
英文摘要：
This paper presents a novel and efficient algorithm for the hierarchical mesh segmentation of 3D meshes, which is based on semi-supervised K-means clustering and k-ring strip growing. The new technique consists of three consecutive stages： prominent feature point extraction, pre-segmentation and post-segmentation. The prominent feature points are extracted using multi-dimensional scaling transformation; the original 3D mesh is then segmented initially using the semi-supervised K-means clustering algorithm in the pre segmentation stage to improve the algorithm＇s efficiency. According to the pre-segmentation results, we apply the Gaussian curvature and k-ring strip growing algorithms to get a hierarchical segmentation of the mesh. Comparing to the other algorithms, our proposed algorithm can obtain better boundaries, and the regions are harmonic.
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