Studies on Shape Feature Combination and Efficient Categorization of 3D Models

Authors:
Tianyang Lv;Guobao Liu;Jiming Pang;Zhengxuan Wang
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China and College of Computer Science and Technology, Harbin Engineering University, Harbin, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Year:
2007

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Abstract

In the field of 3D model retrieval, the combination of different kinds of shape feature is a promising way to improve retrieval performance. And the efficient categorization of 3D models is critical for organizing models. The paper proposes a combination method, which automatically decides the fixed weight of different shape features. Based on the combined shape feature, the paper applies the cluster analysis technique to efficiently categorize 3D models according to their shape. The standard 3D model database, Princeton Shape Benchmark, is adopted in experiment and our method shows good performance not only in improving retrieval performance but also in categorization.