Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Model-based evaluation of clustering validation measures
Pattern Recognition
Consistent mesh partitioning and skeletonisation using the shape diameter function
The Visual Computer: International Journal of Computer Graphics
Upright orientation of man-made objects
ACM SIGGRAPH 2008 papers
Spectral clustering with inconsistent advice
Proceedings of the 25th international conference on Machine learning
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Technical Section: Consistent segmentation of 3D models
Computers and Graphics
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Flexible constrained spectral clustering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Style-content separation by anisotropic part scales
ACM SIGGRAPH Asia 2010 papers
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Photo-inspired model-driven 3D object modeling
ACM SIGGRAPH 2011 papers
Joint shape segmentation with linear programming
Proceedings of the 2011 SIGGRAPH Asia Conference
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
Proceedings of the 2011 SIGGRAPH Asia Conference
Active constrained clustering by examining spectral eigenvectors
DS'05 Proceedings of the 8th international conference on Discovery Science
Unsupervised upright orientation of man-made models
Graphical Models
Co-Segmentation of 3D Shapes via Subspace Clustering
Computer Graphics Forum
Co-hierarchical analysis of shape structures
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Learning part-based templates from large collections of 3D shapes
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Qualitative organization of collections of shapes via quartet analysis
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Sketch2Scene: sketch-based co-retrieval and co-placement of 3D models
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Co-segmentation of 3D shapes via multi-view spectral clustering
The Visual Computer: International Journal of Computer Graphics
Fine-grained semi-supervised labeling of large shape collections
ACM Transactions on Graphics (TOG)
Projective analysis for 3D shape segmentation
ACM Transactions on Graphics (TOG)
SMI 2013: New evaluation metrics for mesh segmentation
Computers and Graphics
SMI 2013: Grouping real functions defined on 3D surfaces
Computers and Graphics
Structure-aware shape processing
SIGGRAPH Asia 2013 Courses
Automatic 3D garment modeling by continuous style description
SIGGRAPH Asia 2013 Posters
CAD/Graphics 2013: Interactive shape co-segmentation via label propagation
Computers and Graphics
Dynamic maps for exploring and browsing shapes
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the shapes alone cannot always fully describe the semantics of the shape parts. In this paper, we propose a semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system. We introduce a novel constrained clustering method based on a spring system which embeds elements to better respect their inter-distances in feature space together with the user-given set of constraints. We also present an active learning method that suggests to the user where his input is likely to be the most effective in refining the results. We show that each single pair of constraints affects many relations across the set. Thus, the method requires only a sparse set of constraints to quickly converge toward a consistent and error-free semantic labeling of the set.