Name
Abstract | ||
|---|---|---|
The analysis for images helps people to gain insights by extracting the inner features and variances between them. However, it is hard to analyze the underlying events further without users participation. We proposes a visual analytic system based on collaborative tagging techniques to allow users to identify features and changes from multi-spectral images. We evaluate our system with mini challenge 3 of VAST Challenge 2017. The exploration results validate the efficiency and effectiveness of our system. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/VAST.2017.8585456 | 2017 IEEE Conference on Visual Analytics Science and Technology (VAST) |
Keywords | Field | DocType |
multispectral images comparisons,visual analytic system,collaborative tagging techniques | Data mining,Histogram,Computer science,Visual analytics,Feature extraction,Multi spectral | Conference |
ISSN | ISBN | Citations |
2325-9442 | 978-1-5386-3164-5 | 0 |
PageRank | References | Authors |
0.34 | 1 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guo-Zheng Li | 1 | 368 | 42.62 |
| Siming Chen | 2 | 125 | 14.34 |
| Qiusheng Li | 3 | 3 | 1.43 |
| Zhibang Jiang | 4 | 0 | 0.34 |
| Yuening Shi | 5 | 0 | 0.34 |
| Qiangqiang Liu | 6 | 0 | 0.68 |
| Xi Liu | 7 | 122 | 20.80 |
| Xiaoru Yuan | 8 | 1157 | 70.28 |