Name
Abstract | ||
|---|---|---|
This work describes an efficient approach for flower classification that is suitable for deployment in mobile devices, allowing its use in a citizen science application for biodiversity monitoring. In the proposed system, geo-located images are uploaded by the user and segmented semi-automatically. We propose a classification method based on histogram comparison of color, shape and texture cues, using metric learning for feature weighting. Our method is tested on the Oxford Flower Dataset and we are able to achieve state-of-the-art accuracy, while proposing an approach that can run efficiently in mobile devices. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2578726.2582620 | ICMR |
Keywords | Field | DocType |
feature weighting,geo-located image,citizen science application,mobile device,histogram comparison,classification method,oxford flower dataset,efficient approach,citizen science mobile app,biodiversity monitoring,flower classification,therapy,gis,kinect | Histogram,Data mining,Weighting,Mobile app,Software deployment,Computer science,Upload,Mobile device,Citizen science,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andréa Britto Mattos | 1 | 12 | 5.58 |
| Ricardo Guimarães Herrmann | 2 | 0 | 0.34 |
| Kelly Kiyumi Shigeno | 3 | 0 | 0.34 |
| Rogério Feris | 4 | 1529 | 89.95 |