Name
Abstract | ||
|---|---|---|
•This work introduces approaches for learning cost functions for graph matching.•This work uses open-set recognition approaches for learning cost functions.•This work uses complex network measurements to encode graph local properties.•The proposed method yields better or comparable results than several baselines. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patrec.2019.08.010 | Pattern Recognition Letters |
Keywords | Field | DocType |
Graph matching,Cost learning,SVM,Open-set recognition | Graph property,Pattern recognition,Graph classification,Matching (graph theory),Artificial intelligence,Classifier (linguistics),Discriminative model,Mathematics,Open set | Journal |
Volume | ISSN | Citations |
128 | 0167-8655 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael de Oliveira Werneck | 1 | 20 | 3.58 |
| Romain Raveaux | 2 | 134 | 15.17 |
| Salvatore Tabbone | 3 | 653 | 52.52 |
| Ricardo da Silva Torres | 4 | 787 | 61.46 |