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

Authors

Jingyu Jiang, 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;
Lili Wan, 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;

Abstract

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.

Recommended Citation

Jiang, Jingyu and Wan, Lili (2020) "Improved Method of Extracting HKS Descriptors and Non-rigid Classification Applications," Journal of System Simulation: Vol. 27: Iss. 10, Article 29.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss10/29

First Page

2422

Revised Date

2015-07-30

DOI Link

https://doi.org/

Last Page

2426

CLC

TP391.41

Recommended Citation

Jiang Jingyu, Wan Lili. Improved Method of Extracting HKS Descriptors and Non-rigid Classification Applications[J]. Journal of System Simulation, 2015, 27(10): 2422-2426.