Two-View Fine-grained Classification of Plant Species

TL;DR

This paper introduces a scalable two-view leaf image representation and hierarchical classification method for fine-grained plant species recognition, leveraging deep metric learning to reduce training data dependence.

Contribution

It proposes a novel two-view leaf image approach combined with hierarchical taxonomy-based classification and Siamese CNNs for scalable, fine-grained plant identification.

Findings

Achieved 87% accuracy on LifeCLEF 2015 dataset.

Achieved 96% accuracy on LeafSnap dataset.

Effective in reducing training data requirements.

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.