Two-View Fine-grained Classification of Plant Species
- URL: http://arxiv.org/abs/2005.09110v2
- Date: Mon, 4 Oct 2021 17:51:21 GMT
- Title: Two-View Fine-grained Classification of Plant Species
- Authors: Voncarlos M. Araujo, Alceu S. Britto Jr., Luiz E. S. Oliveira and
Alessandro L. Koerich
- Abstract summary: We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species.
A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
- Score: 66.75915278733197
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automatic plant classification is a challenging problem due to the wide
biodiversity of the existing plant species in a fine-grained scenario. Powerful
deep learning architectures have been used to improve the classification
performance in such a fine-grained problem, but usually building models that
are highly dependent on a large training dataset and which are not scalable. In
this paper, we propose a novel method based on a two-view leaf image
representation and a hierarchical classification strategy for fine-grained
recognition of plant species. It uses the botanical taxonomy as a basis for a
coarse-to-fine strategy applied to identify the plant genus and species. The
two-view representation provides complementary global and local features of
leaf images. A deep metric based on Siamese convolutional neural networks is
used to reduce the dependence on a large number of training samples and make
the method scalable to new plant species. The experimental results on two
challenging fine-grained datasets of leaf images (i.e. LifeCLEF 2015 and
LeafSnap) have shown the effectiveness of the proposed method, which achieved
recognition accuracy of 0.87 and 0.96 respectively.
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