VEDesc: vertex-edge constraint on local learned descriptors

To improve the performance of local learned descriptors, many researchers pay primary attention to the triplet loss network. As expected, it is useful to achieve state-of-the-art performance on various datasets. However, these local learned descriptors suffer from the inconsistency problem without considering the relationship between two descriptors in a patch. Consequently, the problem causes the irregular spatial distribution of local learned descriptors. In this paper, we propose a neat method to overcome the above inconsistency problem. The core idea is to design a triplet loss function of vertex-edge constraint (VEC), which takes the correlation between two descriptors of a patch into account. Furthermore, to minimize the non-matching descriptors’ influence, we propose an exponential algorithm to reduce the difference between the long and short sides. The competitive performance against state-of-the-art methods on various datasets demonstrates the effectiveness of the proposed method.