Deep metric learning leverages well-designed distance measurement and a sample selection strategy to learn a discriminative feature space. Among the various deep metric learning formulations, triplet loss is built based on a 3-tuple that can simultaneously minimise the distance between the items in the positive pair and maximise the distance between those in the negative pair. However, this endeavour requires a critical selection of triplet samples to guide the training process. In this paper, we propose a layered Triplet loss to solve the fine-grained image classification problem. Unlike the existing triplet loss, which selects samples from only a single criterion, we construct the loss function with the ’coarse to fine’ scheme. This scheme can separate the coarse-level classes while clustering the fine-level samples within a certain margin. An ontology-based sampling method is proposed to enable the network to mine more reasonable hard triplets. Semantic knowledge is employed to assign the visually similar classes to the same learning task, from which hard triplets can be generated. Finally, the softmax tree classifier is used to classify the hierarchical features. The experimental results on multiple datasets demonstrate the effectiveness of the proposed method.

深度度量学习利用精心设计的距离测量和样本选择策略来学习区分特征空间。在各种深度度量学习公式中，三元组损失基于一个三元组建立，该元组可以同时最小化正对项之间的距离，并使负对项之间的距离最大化。但是，此工作需要严格选择三重态样本以指导训练过程。在本文中，我们提出了分层三重态损失来解决细粒度图像分类问题。与现有的三重态损失（仅从一个标准中选择样本）不同，我们使用“从粗到细”方案构造损失函数。这种方案可以将粗略的类分开，同时在一定的范围内将细微的样本聚类。提出了一种基于本体的采样方法，以使网络能够挖掘出更合理的硬三元组。运用语义知识将视觉上相似的类分配给相同的学习任务，从中可以生成三元组。最后，softmax树分类器用于对层次特征进行分类。在多个数据集上的实验结果证明了该方法的有效性。