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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Liu, H. Coquin, D. Valet, L. Cerutti, G. |
| Copyright Year | 2014 |
| Description | Author affiliation: LISTIC, Univ. de Savoie, Annecy le Vieux, France (Liu, H.; Coquin, D.; Valet, L.) || LIRIS, Univ. de Lyon2, Lyon, France (Cerutti, G.) |
| Abstract | Within the framework of a smartphone-based application, helping people to identify plant species in the wild, a sub-classifier strategy has been introduced. It aims at recognizing the botanical properties of a leaf, relatively to various global and local shape criteria used in flora books. A decision function is applied on these classified shape categories to produce a final decision on the species of the leaf. In this paper, the fusion strategy and its corresponding Random-Forest-based sub-classifiers are described. The results of these algorithms for botanical leaf shape recognition demonstrate that our classification algorithm can achieve good performance on leaf species identification while providing the user with relevant information for educational purposes. |
| Starting Page | 1496 |
| Ending Page | 1501 |
| File Size | 667013 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479952090 |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.2014.266 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-24 |
| Publisher Place | Sweden |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Shape Training Radio frequency Vegetation Decision trees Image recognition Feature extraction Belief functions theory Leaf recognition Data Classification Random Forest Dempster-Shafer theory |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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