Abstract. Binary descriptors of image patches are increasingly popular given that they require less storage and enable faster processing. This, however, comes at a price of lower recognition performances. To boost these performances, we project the image patches to a more discriminative subspace, and threshold their coordinates to build our binary descriptor. However, applying complex projections to the patches is slow, which negates some of the advantages of binary descriptors. Hence, our key idea is to learn the discriminative projections so that they can be decomposed into a small number of simple filters for which the responses can be computed fast. We show that with as few as 32 bits per descriptor we outperform the state-of-the-art binary descriptors in terms of both accuracy and efficiency.
D-BRIEF REF projects an image patch onto a more discriminative subspace learned, and then builds the binary descriptor by thresholding their coordinates in the subspace.
Efficient Discriminative Projections for Compact Binary Descriptors
ECCV
Computer Science