Learned BRIEF : transferring the knowledge from hand-crafted to learning-based descriptors


In this paper, we present a novel approach for designing local image descriptors that learn from data and from hand-crafted descriptors. In particular, we construct a learning model that first mimics the behaviour of a hand-crafted descriptor and then learns to improve upon it in an unsupervised manner. We demonstrate the use of this knowledge-transfer framework by constructing the learned BRIEF descriptor based on the well-known hand-crafted descriptor BRIEF. We implement our learned BRIEF with a convolutional autoencoder architecture. Evaluation on the HPatches benchmark for local image descriptors shows the effectiveness of the proposed approach in the tasks of patch retrieval, patch verification, and image matching.

2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)
Nina Žižakić
Doctoral researcher

I completed my PhD at UGent in the field of machine learning for computer vision.