3-D Object Recognition Using Bipartite Matching Embedded in Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete mathematics and its applications (2nd ed.)
Discrete mathematics and its applications (2nd ed.)
Model matching in robot vision by subgraph isomorphism
Pattern Recognition
On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
A graph distance metric based on the maximal common subgraph
Pattern Recognition Letters
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Efficient Attributed Graph Matching and Its Application to Image Analysis
ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
A survey of graph edit distance
Pattern Analysis & Applications
a.SCatch: semantic structure for architectural floor plan retrieval
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Faster subgraph isomorphism detection by well-founded total order indexing
Pattern Recognition Letters
Automatic analysis and sketch-based retrieval of architectural floor plans
Pattern Recognition Letters
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In this paper an extension of index-based subgraph matching is proposed. This extension significantly reduces the storage amount and indexing time for graphs where the nodes are labeled with a rather small amount of different classes. In order to reduce the number of possible permutations, a weight function for labeled graphs is introduced and a well-founded total order is defined on the weights of the labels. Inversions which violate the order are not allowed. A computational complexity analysis of the new preprocessing is given and its completeness is proven. Furthermore, in a number of practical experiments with randomly generated graphs the improvement of the new approach is shown. In experiments performed on random sample graphs, the number of permutations has been decreased to a fraction of 10-18 in average compared to the original approach by Messmer. This makes indexing of larger graphs feasible, allowing for fast detection of subgraphs.