An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Threshold Decomposition of Gray-Scale Morphology into Binary Morphology
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Some MAX SNP-hard results concerning unordered labeled trees
Information Processing Letters
The String-to-String Correction Problem
Journal of the ACM (JACM)
The Tree-to-Tree Correction Problem
Journal of the ACM (JACM)
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shock Graphs and Shape Matching
International Journal of Computer Vision
Mean and maximum common subgraph of two graphs
Pattern Recognition Letters
Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Syntactic Pattern Recognition by Error Correcting Analysis on Tree Automata
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Computing the Edit-Distance between Unrooted Ordered Trees
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Local Similarity in RNA Secondary Structures
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Edit Distance From Graph Spectra
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
An Eigenspace Projection Clustering Method for Inexact Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Polynomial-Time Metrics for Attributed Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
String Kernels for Matching Seriated Graphs
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Graph matching and clustering using spectral partitions
Pattern Recognition
Graph embedding using tree edit-union
Pattern Recognition
Agreeing to disagree: search engines and their public interfaces
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Proof verification and hardness of approximation problems
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
Approximate graph edit distance computation by means of bipartite graph matching
Image and Vision Computing
A strict strong coloring of trees
Information Processing Letters
Comparing stars: on approximating graph edit distance
Proceedings of the VLDB Endowment
Reducing graph matching to tree matching for XML queries with ID references
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Improved MAX SNP-hard results for finding an edit distance between unordered trees
CPM'11 Proceedings of the 22nd annual conference on Combinatorial pattern matching
Inexact graph matching for structural pattern recognition
Pattern Recognition Letters
| Hi-index | 0.10 |
We consider the problem of matching unrooted unordered labeled trees, which refers to the task of evaluating the distance between trees. One of the most famous formalizations of this problem is the computation of the edit distance defined as the minimum-cost sequence of edit operations that transform one tree into another. Unfortunately, this problem has been proved to be NP-complete. In this paper, we propose a new algorithm to measure distance between unrooted unordered labeled trees. This algorithm uses a specific graph coloring to decompose the trees into small components (stars and bistars). Then, it determines a distance between two trees by computing the edit distance between their components. We prove that the proposed distance is a pseudo-metric and we analyze its time complexity. Our experimental evaluations on large synthetic and real world datasets confirm our analytical results and suggest that the distance we propose is accurate and its algorithm is scalable.