Active co-analysis of a set of shapes
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Summary
A semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system, which introduces a novel constrained clustering method which embeds elements to better respect their inter-distances in feature space together with the user-given set of constraints.
- Type
- article
- Published
- 2012-11-01
- Cited by
- 280
- References
- 37
- OpenAlex
- https://openalex.org/W2003940193
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:11944609
Keywords
Set (abstract data type), Computer science, Semantics (computer science), Cluster analysis, Feature (linguistics)
References
- Clustering with Instance-Level Constraints
- From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering
- Semi-supervised distance metric learning for collaborative image retrieval and clustering
- Consistent mesh partitioning and skeletonisation using the shape diameter function
- A survey on Mesh Segmentation Techniques
- Joint shape segmentation with linear programming
- Upright orientation of man-made objects
- Style-content separation by anisotropic part scales
- A benchmark for 3D mesh segmentation
- Co‐Segmentation of 3D Shapes via Subspace Clustering
- Flexible constrained spectral clustering
- Constrained spectral clustering through affinity propagation
- Photo-inspired model-driven 3D object modeling
- Distance Metric Learning for Large Margin Nearest Neighbor Classification
- Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
- Learning 3D mesh segmentation and labeling
- Model-based evaluation of clustering validation measures
- Active Spectral Clustering
- Segmentation given partial grouping constraints
- Normalized cuts and image segmentation
Cited by
- Active learning for semantic labelling of airborne LIDAR data
- Understanding the Structure of Large, Diverse Collections of Shapes
- Management and visualisation of non-linear history of polygonal 3D models
- Cross-class 3D object synthesis guided by reference examples
- Algorithms and Interfaces for Real-Time Deformation of 2D and 3D Shapes
- Data‐Driven Shape Analysis and Processing
- Ergonomics-Inspired Reshaping and Exploration of Collections of Models
- 3D Timeline: Reverse engineering of a part‐based provenance from consecutive 3D models
- Progressive 3D shape segmentation using online learning
- Transductive 3D Shape Segmentation using Sparse Reconstruction
- Data-driven segmentation and labeling of freehand sketches
- Co-hierarchical analysis of shape structures
- A comprehensive overview of methodologies and performance evaluation frameworks in 3D mesh segmentation
- Consistent Mesh Segmentation Based on Shape Diameter Function and EM
- Smart Variations: Functional Substructures for Part Compatibility
- SHED
- Sketch2Scene
- Data-driven structural priors for shape completion
- Projective analysis for 3D shape segmentation
- 3D Shape Segmentation and Labeling via Extreme Learning Machine
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