Twin-Net Descriptor: Twin Negative Mining With Quad Loss for Patch-Based Matching

Abstract

Local keypoint matching is an important step for computer vision based tasks. In recent years, Deep Convolutional Neural Network (CNN) based strategies have been employed to learn descriptor generation to enhance keypoint matching accuracy. Recent state-of-art works in this direction primarily rely upon a triplet based loss function (and its variations) utilizing three samples: an anchor, a positive and a negative. In this work we propose a novel “Twin Negative Mining” based sampling strategy coupled with a Quad loss function to train a deep neural network based pipeline (Twin-Net) for generating a robust descriptor that provides an increased discriminatory power to differentiate between patches that do not correspond to each other. Our sampling strategy and choice of loss function is aimed at placing an upper bound that descriptors of two patches representing same location could be at worst no more dissimilar than the descriptors of two similar looking patches that do-not belong to same 3D location. This results in an increase in the generalization capability of the network and outperforms its existing counterparts when trained over the same datasets. Twin-Net outputs a 128-dimensional descriptor and uses L2 Distance as the similarity metric, and hence conforms to the classical descriptor matching pipelines such as that of SIFT. Our results on Brown and HPatches datasets demonstrate Twin-Net's consistently better performance as well as better discriminatory and generalization capability as compared to the state-of-art.

Keywords

• Descriptor learning
• twin negative sampling
• patch matching
• quad loss