DHNet: working double hard to learn a convolutional neural network-based local descriptor.

Designing effective local descriptors is crucial for many computer vision tasks such as image matching and patch verification. We propose a convolutional neural network (CNN)-based local descriptor named DHNet with a considerate sampling strategy and a dedicated loss function. By considerate sampling, both the closest nonmatching sample and the farther matching sample can be obtained for effectively training a discriminative model. In addition, an improved triplet loss is designed by adding a constraint that limits the absolute distance for the closest nonmatching pair. Based on hard samples and the constraint, our lightweight CNN can quickly generate local descriptors with enhanced intraclass compactness and interclass separation. Experimental results show that our method significantly outperforms the state-of-the-art methods in terms of strong discrimination ability, as evidenced by a considerable performance improvement on several benchmarks. ©2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.4.043008] [ABSTRACT FROM AUTHOR]