OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical morse complexes for piecewise linear 2-manifolds
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
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
Computing the Diameter of a Point Set
DGCI '02 Proceedings of the 10th International Conference on Discrete Geometry for Computer Imagery
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Local and Global Comparison of Continuous Functions
VIS '04 Proceedings of the conference on Visualization '04
Discrete & Computational Geometry
Dominant Sets and Pairwise Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose-Oblivious Shape Signature
IEEE Transactions on Visualization and Computer Graphics
Laplace-Beltrami eigenfunctions for deformation invariant shape representation
SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
A tutorial on spectral clustering
Statistics and Computing
Discrete laplace operator on meshed surfaces
Proceedings of the twenty-fourth annual symposium on Computational geometry
Describing shapes by geometrical-topological properties of real functions
ACM Computing Surveys (CSUR)
A game-theoretic approach to partial clique enumeration
Image and Vision Computing
Technical Section: Consistent segmentation of 3D models
Computers and Graphics
A concise and provably informative multi-scale signature based on heat diffusion
SGP '09 Proceedings of the Symposium on Geometry Processing
3D relevance feedback via multilevel relevance judgements
The Visual Computer: International Journal of Computer Graphics - Special Issue on 3D Object Retrieval 2009
Shape comparison through mutual distances of real functions
Proceedings of the ACM workshop on 3D object retrieval
Shape google: Geometric words and expressions for invariant shape retrieval
ACM Transactions on Graphics (TOG)
Spectral feature selection for shape characterization and classification
The Visual Computer: International Journal of Computer Graphics - Special Issue on 3DOR 2010
Isometric deformation invariant 3D shape recognition
Pattern Recognition
Interactive Search by Direct Manipulation of Dissimilarity Space
IEEE Transactions on Multimedia
A functional-based segmentation of human body scans in arbitrary postures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Co-Segmentation of 3D Shapes via Subspace Clustering
Computer Graphics Forum
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Semantics-driven approach for automatic selection of best views of 3D shapes
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
SHREC'11 track: shape retrieval on non-rigid 3D watertight meshes
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
SHREC'12 track: stability on abstract shapes
EG 3DOR'12 Proceedings of the 5th Eurographics conference on 3D Object Retrieval
PHOG: photometric and geometric functions for textured shape retrieval
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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Scalar functions are widely used to support shape analysis and description. Their role is to sift the most significant shape information and to discard the irrelevant one, acting as a filter for the characteristics that will contribute to the description. Unfortunately, a single property, or function, is not sufficient to characterize a shape and there is not a method to automatically select the functions that better describe a 3D object. Given a set of scalar functions defined on the same object, in this paper we propose a practical approach to automatically group these functions and select a subset of functions that are as much as possible independent of each other. Experiments are exhibited for several datasets to show the suitability of the method to improve and simplify shape analysis and classification issues.