Name
Abstract | ||
|---|---|---|
We consider the problem of how to improve automatic target recognition by fusing the naive sensor-level classification decisions with intuition, or context, in a mathematically principled way. This is a general approach that is compatible with many definitions of context, but for specificity, we consider context as co-occurrence in imagery. In particular, we consider images that contain multiple objects identified at various confidence levels. We learn the patterns of co-occurrence in each context, then use these patterns as hyper-parameters for a Hierarchical Bayesian Model. The result is that low-confidence sensor classification decisions can be dramatically improved by fusing those readings with context. We further use hyperpriors to address the case where multiple contexts may be appropriate. We also consider the Bayesian Network, an alternative to the Hierarchical Bayesian Model, which is computationally more efficient but assumes that context and sensor readings are uncorrelated. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Bayesian inference,Automatic target recognition,Computer science,Intuition,Uncorrelated,Bayesian network,Artificial intelligence,Machine learning,Bayesian probability |
DocType | Volume | ISSN |
Journal | abs/1805.12183 | Proceedings of the National Fire Control Symposium, February 2018 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher A. George | 1 | 0 | 0.68 |
| Pranab Banerjee | 2 | 3 | 1.12 |
| Kendra E. Moore | 3 | 27 | 4.40 |