BELID: Boosted Efficient Local Image Descriptor

摘要

Efficient matching of local image features is a fundamental task in many computer vision applications. Real-time performance of top matching algorithms is compromised in computationally limited devices, due to the simplicity of hardware and the finite energy supply. In this paper we present BELID, an efficient learned image descriptor. The key for its efficiency is the discriminative selection of a set of image features with very low computational requirements. In our experiments, performed both in a personal computer and a smartphone, BEbID has an accuracy similar to SIFT with execution times comparable to ORB, the fastest algorithm in the literature.