系统仿真学报 ›› 2015, Vol. 27 ›› Issue (10): 2422-2426.

• 虚拟现实与可视化 • 上一篇    下一篇

一种改进的HKS提取方法及非刚体分类应用

江静宇1,2, 万丽莉1,2   

  1. 1.北京交通大学计算机与信息技术学院信息科学研究所,北京 100044;
    2.北京交通大学现代信息科学与网络技术北京市重点实验室,北京 100191
  • 收稿日期:2015-06-14 修回日期:2015-07-30 出版日期:2015-10-08 发布日期:2020-08-07
  • 作者简介:江静宇(1991-),男,河南,硕士,研究方向为模式识别;万丽莉(1979-),女,湖北,博士,副教授,研究方向为三维模型检索、虚拟现实。
  • 基金资助:
    国家自然科学基金项目(61572064); 中央高校基本科研业务费专项资金(2014JBM027)

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

Jiang Jingyu1,2, Wan Lili1,2   

  1. 1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;
    2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2015-06-14 Revised:2015-07-30 Online:2015-10-08 Published:2020-08-07

摘要: 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。

关键词: 热核特征, 非刚体, 特征提取, 稀疏表示

Abstract: 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.

Key words: heat kernel signature, non-rigid object, feature extraction, sparse representation

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