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.
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Lv, Ty., Xing, Yh., Huang, Sb., Wang, Zx., Zuo, Wl. (2005). An Auto-stopped Hierarchical Clustering Algorithm for Analyzing 3D Model Database. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_63
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DOI: https://doi.org/10.1007/11564126_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29244-9
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