Learning Spread-out Local Feature Descriptors

Understand

We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors.

• The idea is that in order to fully utilize the expressive power of the descriptor space, good local feature descriptors should be sufficiently "spread-out" over the space.
• In this work, we propose a regularization term to maximize the spread in feature descriptor inspired by the property of uniform distribution.
• We show that the proposed regularization with triplet loss outperforms existing Euclidean distance based descriptor learning techniques by a large margin.