Name
Abstract | ||
|---|---|---|
Graph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, the proposed algorithm sequentially performs a sampling and importance resampling to maximize the graph matching objective. Through the sequential sampling procedure, the algorithm effectively collects potential matches under one-to-one matching constraints to avoid the adverse effect of outliers and deformation. Experimental evaluations on synthetic graphs and real images demonstrate its higher robustness to deformation and outliers. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33712-3_45 | ECCV (3) |
Keywords | Field | DocType |
sequential monte carlo,experimental evaluation,sequential sampling procedure,computer vision,sequential monte carlo framework,proposed algorithm sequentially,adverse effect,synthetic graph,higher robustness,one-to-one matching constraint,graph matching,object recognition | Pattern recognition,Computer science,Particle filter,Outlier,Robustness (computer science),Matching (graph theory),Artificial intelligence,Sampling (statistics),Real image,3-dimensional matching,Resampling,Machine learning | Conference |
Volume | ISSN | Citations |
7574 | 0302-9743 | 23 |
PageRank | References | Authors |
0.64 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yumin Suh | 1 | 49 | 4.38 |
| Minsu Cho | 2 | 677 | 35.74 |
| Kyoung Mu Lee | 3 | 3228 | 153.84 |