An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional object recognition from single two-dimensional images
Artificial Intelligence
An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph isomorphism is in the low hierarchy
Journal of Computer and System Sciences
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear and Context-Free Graph Grammars
Journal of the ACM (JACM)
A Linear Programming Approach for the Weighted Graph Matching Problem
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network approach for solving the maximal common subgraph problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A personalized graph-based document ranking model using a semantic user profile
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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In this paper a new distance for attributed relational graphs is proposed. The main idea of the new algorithm is to decompose the graphs to be matched into smaller subgraphs. The matching process is then done at the level of the decomposed subgraphs based on the concept of error-correcting transformations. The distance between two graphs is found to be the minimum of a weighted bipartite graph constructed from the decomposed subgraphs. The average computational complexity of the proposed distance is found to be O(N4), which is much better than many techniques.