Name
Abstract | ||
|---|---|---|
In high spatial resolution (HSR) imagery scene classification, it is a challenging task to recognize the high-level semantics from a large volume of complex HSR images. The probabilistic topic model (PTM), which focuses on modeling topics, has been proposed to bridge the so-called semantic gap. Conventional PTMs usually model the images with a dense semantic representation and, in general, one top... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TGRS.2017.2709802 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Semantics,Training,Feature extraction,Probabilistic logic,Visualization,Spatial resolution,Robustness | Computer science,Robustness (computer science),Artificial intelligence,Probabilistic logic,Discriminative model,Computer vision,Pattern recognition,Visualization,Semantic gap,Feature extraction,Topic model,Machine learning,Semantics | Journal |
Volume | Issue | ISSN |
55 | 10 | 0196-2892 |
Citations | PageRank | References |
4 | 0.40 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiqi Zhu | 1 | 29 | 3.55 |
| Yanfei Zhong | 2 | 1044 | 90.58 |
| Liangpei Zhang | 3 | 5448 | 307.02 |
| Deren Li | 4 | 620 | 74.26 |