On Median Graphs: Properties, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Saliency, Scale and Image Description
International Journal of Computer Vision
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Central Clustering of Attributed Graphs
Machine Learning
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Spatial Priors for Part-Based Recognition Using Statistical Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Protein classification by matching and clustering surface graphs
Pattern Recognition
Bayesian optimization of the scale saliency filter
Image and Vision Computing
Graph clustering using the weighted minimum common supergraph
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
ACM attributed graph clustering for learning classes of images
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Learning a Generative Model for Structural Representations
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
3D Object Recognition Using Hyper-Graphs and Ranked Local Invariant Features
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Clustering Using Class Specific Hyper Graphs
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Region and constellations based categorization of images with unsupervised graph learning
Image and Vision Computing
Learning Class Specific Graph Prototypes
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
On the Computation of the Common Labelling of a Set of Attributed Graphs
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
The Journal of Machine Learning Research
Graduated assignment algorithm for finding the common labelling of a set of graphs
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Learning graph prototypes for shape recognition
Computer Vision and Image Understanding
Models and algorithms for computing the common labelling of a set of attributed graphs
Computer Vision and Image Understanding
Parallel graduated assignment algorithm for multiple graph matching based on a common labelling
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
A probabilistic framework to obtain a common labelling between attributed graphs
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Supervised learning of graph structure
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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In this paper, we propose a novel method for the unsupervised clustering of graphs in the context of the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of fast matching with graph transformations. Our experiments, both with random graphs and in realistic situations (visual localization), show that our prototypes improve the set median graphs and also the prototypes derived from our previous incremental method. We also discuss how the method scales with a growing number of images.