Name
Title | ||
|---|---|---|
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning. |
Abstract | ||
|---|---|---|
•This paper proposes a unified framework to perform classification and low-level modeling jointly.•Robustness is improved by considering a possibly badly labeled training set.•The proposed model allows a very rich interpretation of the modeled data structure.•Performance is assessed on synthetic and real data in the specific context of hyperspectral image interpretation.•The proposed model is generic enough to incorporate any kind of low-level modeling. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2018.07.026 | Pattern Recognition |
Keywords | DocType | Volume |
Bayesian model,Supervised learning,Image interpretation,Markov random field | Journal | 85 |
Issue | ISSN | Citations |
1 | 0031-3203 | 2 |
PageRank | References | Authors |
0.35 | 23 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrien Lagrange | 1 | 13 | 2.63 |
| Mathieu Fauvel | 2 | 742 | 42.30 |
| Stéphane May | 3 | 4 | 4.15 |
| Nicolas Dobigeon | 4 | 2070 | 108.02 |