A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
ACM Transactions on Graphics (TOG)
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Feature-based similarity search in 3D object databases
ACM Computing Surveys (CSUR)
Selecting Distinctive 3D Shape Descriptors for Similarity Retrieval
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Unsupervised learning from a corpus for shape-based 3D model retrieval
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A spectral approach to shape-based retrieval of articulated 3D models
Computer-Aided Design
Laplace-Beltrami eigenfunctions for deformation invariant shape representation
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
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
A survey of content based 3D shape retrieval methods
Multimedia Tools and Applications
Structural Shape Prototypes for the Automatic Classification of 3D Objects
IEEE Computer Graphics and Applications
Technical Section: Discrete Laplace-Beltrami operators for shape analysis and segmentation
Computers and Graphics
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids
Computer-Aided Design
A concise and provably informative multi-scale signature based on heat diffusion
SGP '09 Proceedings of the Symposium on Geometry Processing
Approximating gradients for meshes and point clouds via diffusion metric
SGP '09 Proceedings of the Symposium on Geometry Processing
A probability-based unified 3d shape search
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
Shape comparison through mutual distances of real functions
Proceedings of the ACM workshop on 3D object retrieval
Bag of words and local spectral descriptor for 3D partial shape retrieval
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
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In the context of shape matching, this paper proposes a framework for selecting the Laplacian eigenvalues of 3D shapes that are more relevant for shape comparison and classification. Three approaches are compared to identify a specific set of eigenvalues such that they maximise the retrieval and/or the classification performance on the input benchmark data set: the first k eigenvalues, by varying k over the cardinality of the spectrum; the Hill Climbing technique; and the AdaBoost algorithm. In this way, we demonstrate that the information coded by the whole spectrum is unnecessary and we improve the shape matching results using only a set of selected eigenvalues. Finally, we test the efficacy of the selected eigenvalues by coupling shape classification and retrieval.