Name
Abstract | ||
|---|---|---|
In this work, we introduce a rapid and accurate flower/leaf recognition system. The system could process one query in less than 0.35s with users' simple interaction. Meanwhile, high accuracy and recall is achieved. Furthermore, low computational resource and memory cost are required by the system. Now, the system is demonstrated on 172 categories of flowers, the largest flower dataset until now, and 220 categories of leaves. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2393347.2396430 | ACM Multimedia 2001 |
Keywords | Field | DocType |
rapid flower,largest flower dataset,memory cost,leaf recognition system,simple interaction,low computational resource,high accuracy,accurate flower | Computer vision,Computer science,Leaf recognition,Artificial intelligence,Recall,Machine learning,Computational resource | Conference |
Citations | PageRank | References |
1 | 0.36 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianbiao Qi | 1 | 103 | 8.25 |
| Rong Xiao | 2 | 559 | 36.27 |
| Lei Zhang | 3 | 3 | 0.73 |
| Chun-Guang Li | 4 | 310 | 17.35 |
| Jun Guo | 5 | 1579 | 137.24 |