Local image descriptors that are highly discriminative,
computational efficient, and with low storage footprint have
long been a dream goal of computer vision research. In this
paper, we focus on learning such descriptors, which make
use of the DAISY configuration and are simple to compute
both sparsely and densely. We develop a new training set of
match/non-match image patches which improves on previous
work. We test a wide variety of gradient and steerable
filter based configurations and optimize over all parameters
to obtain low matching errors for the descriptors. We
further explore robust normalization, dimension reduction
and dynamic range reduction to increase the discriminative
power and yet reduce the storage requirement of the learned
descriptors. All these enable us to obtain highly efficient local
descriptors: e.g, 13:2% error at 13 bytes storage per descriptor,
compared with 26:1% error at 128 bytes for SIFT.