Name
Abstract | ||
|---|---|---|
A number of automated techniques for classification of plants based on their leaves have been developed over the past few years. While each of those techniques have been individually implemented and evaluated, but there have been few studies which have made a direct comparison between the various techniques. In this paper we compared the three well-known techniques. We compared their ability to differentiate among plant species. Techniques which are evaluated are, Histogram of Oriented Gradient (HOG),Colour Scale Invariant Feature Transform (C-SIFT) and Maximally Stable Extremal Region (MSER).These techniques are evaluated against two kinds of leaf datasets, one is our personal built dataset and the other is famous Flavia dataset. The experimental results shows that HOG has an accuracy of 98% on our dataset and 97% for Flavia dataset. Moreover for C-Sift the accuracy for both datasets is 98% and for MSER the accuracy is 96% and 90% for our and Flavia dataset respectively. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DICTAP.2016.7544015 | 2016 Sixth International Conference on Digital Information and Communication Technology and its Applications (DICTAP) |
Keywords | Field | DocType |
Leaf Recognition,SVM classifier,HOG,CSift,MSER,Flavia,Confusion matrix | Histogram,Computer vision,Scale-invariant feature transform,Pattern recognition,Computer science,Artificial intelligence,Plant species | Conference |
ISSN | ISBN | Citations |
2377-858X | 978-1-4673-9610-3 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Syed Yasser Arafat | 1 | 4 | 0.80 |
| Muhammad Inzimam Saghir | 2 | 0 | 0.34 |
| Mubah Ishtiaq | 3 | 0 | 0.34 |
| Umer Bashir | 4 | 0 | 0.34 |