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

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.

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

CLC Number:

TP391.41

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.

References

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