Graphical evolution: an introduction to the theory of random graphs
Graphical evolution: an introduction to the theory of random graphs
Principles of artificial intelligence
Principles of artificial intelligence
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene analysis using appearance-based models and relational indexing
ISCV '95 Proceedings of the International Symposium on Computer Vision
Combining Two Structured Domains for Modeling Various Graph Matching Problems
Recent Advances in Constraints
Hi-index | 0.10 |
The 'LeRP' algorithm approximates subgraph isomorphism for attributed graphs based on counts of length-r paths. The algorithm provides a good approximation to the maximal isomorphic subgraph. The basic approach of the LeRP algorithm differs fundamentally from other methods. When comparing structural similarity LeRP uses a neighborhood of nodes that varies in size dynamically. This approach provides sufficient evidence of similarity to permit LeRP to form a node-to-node mapping and can be computed with polynomial effort in the worst-case. Results from over 32,000 simulated cases are reported. We demonstrate that LeRP does not need a high dynamic range of node and edge coloring to perform well. For example, LeRP can input 50-node and 100-node graphs that contain a common 50-node subgraph, and then compute a matching subgraph having 49.74 ± 0.46 nodes (mean ± one standard deviation). This takes from 0.4 to 0.5 s. In this example, 100 trials were evaluated and graphs had discrete coloring for nodes and edges with a dynamic range of four, Test conditions are varied and include strongly regular graphs as well as Model A.