An auto-stopped hierarchical clustering algorithm for analyzing 3d model database

Authors:
Tian-yang Lv;Yu-hui Xing;Shao-bing Huang;Zheng-xuan Wang;Wan-li Zuo
Affiliations:
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, 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
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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Abstract

In the research of shape-based 3D model retrieval, the analysis and classification of 3D model database is an important topic for improving the retrieval performance. However, it encounters difficulties due to lack of valuable prior knowledge and the semantic gaps exist in 3D model retrieval. The paper proposes a new auto-stopped hierarchical clustering algorithm overcome these problems, which combines outlier detection with clustering. The Princeton Shape Benchmark along with 2 data sets from UCI is employed to evaluate the performance of the algorithm. And the new algorithm outperforms other auto-stopped algorithms and obtains better classification of 3D model database.