An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
An energy function and continuous edit process for graph matching
Neural Computation
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Subgraph Isomorphism
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Shock Graphs and Shape Matching
International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On Median Graphs: Properties, Algorithms, and Applications
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Recognizing Indoor Images with Unsupervised Segmentation and Graph Matching
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A survey of kernels for structured data
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Central Clustering of Attributed Graphs
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Pictorial Structures for Object Recognition
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Graph Edit Distance from Spectral Seriation
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Spatial Priors for Part-Based Recognition Using Statistical Models
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One-Shot Learning of Object Categories
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
Bottom-Up & Top-down Object Detection using Primal Sketch Features and Graphical Models
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Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
Protein classification by matching and clustering surface graphs
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Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reeb graphs for shape analysis and applications
Theoretical Computer Science
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
Szemerédi's regularity lemma and its applications to pairwise clustering and segmentation
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Learning generative graph prototypes using simplified von neumann entropy
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Entropy and Distance of Random Graphs with Application to Structural Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Graphs: Structural-Contextual Dichotomy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching through entropic manifold alignment
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Two Bayesian methods for junction classification
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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In this paper, we exploit graph kernels for graph matching and clustering. Firstly, we analyze different kinds of graph kernels in order to extract from them attributes to be used as a similarity measure between nodes of non-attributed graphs. Next, such attributes are embedded in a graph-matching cost function, through a probabilistic framework, and we evaluate their performance within a graph-matching algorithm. Secondly, we propose a method for obtaining a representative prototype from a set of graphs, which relies on obtaining all the pairwise matchings between the input graphs, and uses the information provided by graph kernels in order to select the matchings that will be considered for obtaining the prototype. Nodes and edges in such a prototype graph register their frequency of occurrence, so that it can be considered a first-order generative model. The proposed method for building prototypes is efficiently integrated into a central clustering algorithm, which allows us to unsupervisedly learn the class-structure of a given set of graphs, and the prototypes representing each class, thus obtaining a central graph clustering algorithm with the same computational cost than a pairwise one. We successfully apply the proposed methods to structural recognition problems.