Approximating the permanent via importance sampling with application to the dimer covering problem
Journal of Computational Physics
Curvature Scale Space Corner Detector with Adaptive Threshold and Dynamic Region of Support
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Polynomial-Time Metrics for Attributed Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Neural Computation
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A spectral approach to learning structural variations in graphs
Pattern Recognition
Spectral Generative Models for Graphs
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Retrieving articulated 3-D models using medial surfaces
Machine Vision and Applications
Learning a Generative Model for Structural Representations
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Graph clustering using the weighted minimum common supergraph
GbRPR'03 Proceedings of the 4th IAPR international conference on Graph based representations in pattern recognition
Constellations and the unsupervised learning of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Network Inference From Co-Occurrences
IEEE Transactions on Information Theory
Attributed graph similarity from the quantum jensen-shannon divergence
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learning structural representations from sets of training examples is relatively limited. In this paper we take a simple yet effective Bayesian approach to attributed graph learning. We present a naïve node-observation model, where we make the important assumption that the observation of each node and each edge is independent of the others, then we propose an EM-like approach to learn a mixture of these models and a Minimum Message Length criterion for components selection. Moreover, in order to avoid the bias that could arise with a single estimation of the node correspondences, we decide to estimate the sampling probability over all the possible matches. Finally we show the utility of the proposed approach on popular computer vision tasks such as 2D and 3D shape recognition.