Local Feature Descriptor Learning with a Dual Hard Sampling Strategy

Local feature descriptor learning based on Convolutional Neural Network (CNN) has demonstrated its capability to generate descriptors with high quality. While extensive studies focused on mining hard non-matching examples to improve descriptor learning performance, a random sampling strategy is adopted for matching examples. In this paper, a dual hard sampling strategy based on the triplet loss function is proposed to generate the hard matching and non-matching examples for training. To start with, a pair of matching examples with the maximum distance for each class are selected as the positive pair. For each positive pair, their closest non-matching example is then sampled from the generated positive pairs with other classes as the corresponding negative. Based on the above dual hard sampling strategy, a novel triplet loss function is presented for optimization. With the benefits of the proposed sampling strategy and the novel triplet loss function, our method achieves better performance compared to state-of-the-art on the reference benchmark for local feature matching.