Book chapter
Supervised learning of similarity measures for content-based 3D model retrieval
Large-Scale Knowledge Resources. Construction and Application, Vol.4938, pp.210-225
Springer Berlin Heidelberg
2008
Abstract
In this paper we investigate on how the choice of similarity measures affects the performance of content-based 3D model retrieval (CB3DR) algorithms. In CB3DR, shape descriptors are used to provide a numerical representation of the salient features of the data, while similarity functions capture the high level semantic concepts. In the first part of the paper, we demonstrate experimentally that the Euclidean distance is not the optimal similarity function for 3D model classification and retrieval. Then, in the second part, we propose to use a supervised learning approach for automatic selection of the optimal similarity measure that achieves the best performance. Our experiments using the Princeton Shape Benchmark (PSB) show significant improvements in the retrieval performance.
Details
- Title
- Supervised learning of similarity measures for content-based 3D model retrieval
- Authors/Creators
- H. Laga (Author/Creator) - Tokyo Institute of TechnologyM. Nakajima (Author/Creator) - Tokyo Institute of Technology
- Contributors
- T. Tkunaga (Editor)A. Ortega (Editor)
- Publication Details
- Large-Scale Knowledge Resources. Construction and Application, Vol.4938, pp.210-225
- Publisher
- Springer Berlin Heidelberg
- Identifiers
- 991005540150407891
- Copyright
- 2008 Springer-Verlag Berlin Heidelberg
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Book chapter
- Additional Information
- Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Tokyo, Japan, March 3-5, 2008. Proceedings Lecture Notes in Computer Science; Vol. 4938
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