Name
Abstract | ||
|---|---|---|
Plant identification belongs to a specific application domain of data mining. Images of plant leaves are usually used as the main element to distinguish a plant from another. For proper identification, feature extraction is necessary. In the literature, most plant recognition systems use the features along with a classification method, which has been adapted or modified to face this type of application. In this paper, we propose three new geometric features that describe the vertical and horizontal symmetry of leaves. These features are simple to extract from images. According to the results of experiments, when these features are used in conjunction with other well-known geometric characteristics, the performance of classical classification methods is remarkably improved. To show the effectiveness of the proposal, we test seven classifiers with images of leaves publicly available on the Internet. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICNSC.2016.7479024 | 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC) |
Keywords | Field | DocType |
Plant recognition,feature extraction,leaf identification,geometric feature | Data mining,Horizontal and vertical,Pattern recognition,Feature (computer vision),Computer science,Feature extraction,Feature (machine learning),Artificial intelligence,Application domain,Plant identification,The Internet | Conference |
ISSN | Citations | PageRank |
1810-7869 | 1 | 0.35 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Rojas-Hernandez | 1 | 1 | 0.35 |
| Asdrúbal López Chau | 2 | 87 | 11.62 |
| Valentin Trujillo-Mora | 3 | 1 | 0.35 |
| Carlos A. Rojas-Hernandez | 4 | 1 | 0.35 |