Three-dimensional alpha shapes
ACM Transactions on Graphics (TOG)
Programs to generate Niederreiter's low-discrepancy sequences
ACM Transactions on Mathematical Software (TOMS)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Modern Information Retrieval
ACM Transactions on Graphics (TOG)
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Content based retrieval of VRML objects: an iterative and interactive approach
Proceedings of the sixth Eurographics workshop on Multimedia 2001
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Shape-Similarity Comparison of 3D Models Using Alpha Shapes
PG '03 Proceedings of the 11th Pacific Conference on Computer Graphics and Applications
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
Learning an image manifold for retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Automatic Selection and Combination of Descriptors for Effective 3D Similarity Search
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Feature Combination and Relevance Feedback for 3D Model Retrieval
MMM '05 Proceedings of the 11th International Multimedia Modelling Conference
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Sub-sampling for efficient spectral mesh processing
CGI'06 Proceedings of the 24th international conference on Advances in Computer Graphics
Learning semantic categories for 3D model retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
A boosting approach to content-based 3D model retrieval
Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia
Comparison of Dimension Reduction Methods for Database-Adaptive 3D Model Retrieval
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Component based shape retrieval using differential profiles
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Supervised learning of similarity measures for content-based 3D model retrieval
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Hybrid associative retrieval of three-dimensional models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
From 2D silhouettes to 3D object retrieval: contributions and benchmarking
Journal on Image and Video Processing
Multimodal search and retrieval using manifold learning and query formulation
Proceedings of the 16th International Conference on 3D Web Technology
SHREC'09 track: generic shape retrieval
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
Feature selection for enhanced spectral shape comparison
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
A unified framework for multimodal retrieval
Pattern Recognition
Learning kernels on extended Reeb graphs for 3d shape classification and retrieval
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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Arguably the most important issues in shape-based 3D model retrieval are methods to extract powerful yet compact shape features and methods to properly and promptly compare the shape features. In this paper, we explore a method to improve feature distance computation by employing unsupervised learning of the subspace of 3D shape features from a corpus. We employ an algorithm called Laplacian Eigenmaps proposed by Belkin, et al. to learn a manifold spanned by shape features of 3D models in the corpus. The learned manifold is approximated by an RBF network, onto which shape features are projected. Distances among shape features can then be computed effectively on the learned manifold. We combine this learning-based distance-computation method with a method to extract multiresolution shape features proposed by Ohbuchi, et al. Our experimental evaluation showed that the proposed method could significantly improve retrieval performance. Learning alone improved performance of two shape features we tried by about 5%. A combination of learning and multiresolution shape feature allowed about 10% gain in performance. As an example, the trained, multiresolution version of the SPRH gained 10% over the original single resolution, untrained SPRH shape feature.