一种改进的HKS提取方法及非刚体分类应用

Improved Method of Extracting HKS Descriptors and Non-rigid Classification Applications

为了使热核特征HKS(heat kernel signature)在非刚体形状分析研究中具有更广的适用性,对于非连通的非刚体三维模型提出一种改进的HKS提取方法。提取模型的最大连通集,计算最大连通集的顶点HKS特征,并从顶点特征集合中排除边界点及其一阶邻域点的特征。在形状分类应用中,结合稀疏表示理论,对于训练数据中的每类非刚体三维模型均训练出一个字典,分别用每类的字典对待分类模型的特征集合进行稀疏表示,以确定最合适的字典,从而判断出待分类模型的类别。实验结果表明,该方法具有较高的分类准确率。

In order to make the HKS(heat kernel signature)have wider applicability in non-rigid shape analysis, an improved method of extracting HKS descriptors for unconnected non-rigid 3D models was proposed. The largest connected component was obtained. The HKS descriptors of the largest connected component were calculated and those descriptors of the boundary vertices and their 1-ring neighbors were excluded. For shape classifications, the dictionary was learned for each class based on the sparse representation theory. For a test model, each dictionary was utilized to sparsely represent its descriptor set, and the most appropriate dictionary was determined by the representation error, the model was classified according to this dictionary. Experimental results show the proposed method has good classification accuracy.