STCDesc: Learning deep local descriptor using similar triangle constraint

摘要

Triplet loss is widely used to detect learned descriptors and achieves promising performance. However, triplet loss fails to fully consider the influence of adjacent descriptors from the same type of sample, which is one of the main reasons for image mismatching. To solve this problem, we propose a descriptor network based on triplet loss with a similar triangle constraint, named as STCDesc. This network not only considers the correlation between descriptors from different types of samples but also considers the relevance of descriptors from the same type of sample. Furthermore, we propose a normalized exponential algorithm to reduce the impact of negative samples and improve calculation speed. The proposed method can effectively improve the stability of learned descriptors using the proposed triangle constraint and normalized exponential algorithm. To verify the effectiveness of the proposed descriptor network, extensive experiments were conducted using four benchmarks. The experimental results demonstrate that the proposed descriptor network achieves favorable performance compared to state-of-the-art methods.