Algorithms for clustering data
Algorithms for clustering data
Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
On the editing distance between unordered labeled trees
Information Processing Letters
Organizing Large Structural Modelbases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic operators for hierarchical graph clustering
Pattern Recognition Letters
Shock Graphs and Shape Matching
International Journal of Computer Vision
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Mean and maximum common subgraph of two graphs
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Approach to Grouping
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Dominant Sets and Hierarchical Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Central Clustering of Attributed Graphs
Machine Learning
A skeletal measure of 2D shape similarity
Computer Vision and Image Understanding
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial-Time Metrics for Attributed Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine 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
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
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
Graph-Based Representations in Pattern Recognition and Computational Intelligence
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Indexing tree and subtree by using a structure network
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Graph embedding for pattern recognition
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Graph embedding using constant shift embedding
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Entropy versus heterogeneity for graphs
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Characterizing graphs using approximate von Neumann entropy
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Graph clustering using the Jensen-Shannon Kernel
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Pattern analysis with graphs: Parallel work at Bern and York
Pattern Recognition Letters
Hypergraph-based image retrieval for graph-based representation
Pattern Recognition
Graph characterizations from von Neumann entropy
Pattern Recognition Letters
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Coloring based approach for matching unrooted and/or unordered trees
Pattern Recognition Letters
Graph Kernels from the Jensen-Shannon Divergence
Journal of Mathematical Imaging and Vision
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In this paper we address the problem of how to learn a structural prototype that can be used to represent the variations present in a set of trees. The prototype serves as a pattern space representation for the set of trees. To do this we construct a super-tree to span the union of the set of trees. This is a chicken and egg problem, since before the structure can be estimated correspondences between the nodes of the super-tree and the nodes of the sample tree must be to hand. We demonstrate how to simultaneously estimate the structure of the super-tree and recover the required correspondences by minimizing the sum of the tree edit-distances over pairs of trees, subject to edge consistency constraints. Each node of the super-tree corresponds to a dimension of the pattern space, and for each tree we construct a pattern vector in which the elements of the weights corresponding to each of the dimensions of the super-tree. We perform pattern analysis on the set of trees by performing principal components analysis on the vectors. The method is illustrated on a shape analysis problem involving shock-trees extracted from the skeletons of 2D objects.