论文标题
两种植物物种的细颗粒分类
Two-View Fine-grained Classification of Plant Species
论文作者
论文摘要
自动植物分类是一个具有挑战性的问题，因为在细粒度的情况下现有植物物种的生物多样性广泛。强大的深度学习体系结构已被用来在如此细粒度的问题中改善分类性能，但通常建立高度依赖大型培训数据集并且不可扩展的模型。在本文中，我们提出了一种基于两种叶子图像表示形式的新方法和一种用于植物物种细粒识别的分层分类策略。它使用植物分类法作为用于识别植物属和物种的粗到精细策略的基础。两视图表示叶子图像的互补全球和局部特征。基于暹罗卷积神经网络的深度度量用于减少对大量训练样本的依赖，并使该方法可扩展到新植物物种。在两个具有挑战性的叶片图像数据集（即Lifeclef 2015和Leafsnap）上的实验结果显示了该方法的有效性，该方法的效果分别达到了0.87和0.96的识别精度。
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.
