Local feature descriptor learning aims to represent distinctive images or patches with the same local features, where their representation is invariant under different types of deformation. Recent studies have demonstrated that descriptor learning based on Convolutional Neural Network (CNN) is able to improve the matching performance significantly. However, they tend to ignore the importance of sample selection during the training process, leading to unstable quality of descriptors and learning efficiency. In this paper, a dual hard batch construction method is proposed to sample the hard matching and non-matching examples for training, improving the performance of the descriptor learning on different tasks. To construct the dual hard training batches, the matching examples with the minimum similarity are selected as the hard positive pairs. For each positive pair, the most similar non-matching example is then sampled from the generated hard positive pairs in the same batch as the corresponding negative. By sampling the hard positive pairs and the corresponding hard negatives, the hard batches are produced to force the CNN model to learn the descriptors with more efforts. In addition, based on the above dual hard batch construction, an $\ell _{2}^{2}$ triplet loss function is built for optimizing the training model. Specifically, we analyze the superiority of the $\ell _{2}^{2}$ loss function when dealing with hard examples, and also demonstrate it in the experiments. With the benefits of the proposed sampling strategy and the $\ell _{2}^{2}$ triplet loss function, our method achieves better performance compared to state-of-the-art on the reference benchmarks for different matching tasks.

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具有双重硬批构造的深度局部特征描述符学习

局部特征描述符学习旨在表示具有相同局部特征的独特图像或面片，其中它们的表示在不同类型的变形下是不变的。最近的研究表明，基于卷积神经网络（CNN）的描述符学习能够显着提高匹配性能。但是，他们倾向于忽略训练过程中样本选择的重要性，从而导致描述符质量和学习效率不稳定。本文提出了一种双重硬批构造方法，对硬匹配和非匹配示例进行训练，以提高描述子在不同任务上的学习性能。为了构造双重硬训练批次，选择具有最小相似性的匹配示例作为硬正对。对于每个正对，然后从与相应负数相同的批次中从生成的硬正对中采样最相似的不匹配示例。通过采样硬正对和相应的硬负对，可以生产硬批，以迫使CNN模型更加努力地学习描述符。另外，基于上述双重硬批构造， $ \ ell _ {2} ^ {2} $ 建立三重损失函数可优化训练模型。具体来说，我们分析了 $ \ ell _ {2} ^ {2} $ 处理困难示例时的损失函数，并在实验中进行演示。受益于拟议的抽样策略和 $ \ ell _ {2} ^ {2} $ 三重损失函数，与针对不同匹配任务的参考基准的最新技术相比，我们的方法具有更好的性能。