Name
Abstract | ||
|---|---|---|
Flower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two-step deep learning classifier to distinguish flowers of a wide range of species. First, the flower region is automatically segmented to allow localisation of the minimum bounding b... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1049/iet-cvi.2017.0155 | IET Computer Vision |
Keywords | Field | DocType |
biology computing,botany,feedforward neural nets,learning (artificial intelligence),object recognition,pattern classification | Pattern recognition,Binary classification,Segmentation,Convolutional neural network,Artificial intelligence,Deep learning,Classifier (linguistics),Mathematics,Minimum bounding box | Journal |
Volume | Issue | ISSN |
12 | 6 | 1751-9632 |
Citations | PageRank | References |
1 | 0.48 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hazem Hiary | 1 | 46 | 7.68 |
| Heba Saadeh | 2 | 17 | 1.00 |
| Maha Saadeh | 3 | 10 | 2.67 |
| Mohammad Yaqub | 4 | 84 | 7.72 |