Proceedings of the 12th annual ACM international conference on Multimedia
Computers & Mathematics with Applications
Structural Object Recognition Using Entropy Correspondence Measure of Line Features
IEICE - Transactions on Information and Systems
Graph Matching Based on Node Signatures
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Attributed relational graph matching based on the nested assignment structure
Pattern Recognition
Machine learning problems from optimization perspective
Journal of Global Optimization
Synthesis of median spectral graph
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
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An attributed graph (AG) is a useful data structure for representing complex patterns in a wide range of applications such as computer vision, image database retrieval, and other knowledge representation tasks where similar or exact corresponding structural patterns must be found. Existing methods for attributed graph matching (AGM) often suffer from the combinatorial problem whereby the execution cost for finding an exact or similar match is exponentially related to the number of nodes the AG contains. The square matching error of two AGs subject to permutations is approximately relaxed to a square matching error of two AGs subject to orthogonal transformations. Hence, the principal component analysis (PCA) algorithm can be used for the fast computation of the approximate matching error, with a considerably reduced execution complexity. Experiments demonstrate that this method works well and is robust against noise and other simple types of transformations