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.

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In  Computer Vision and Pattern Recognition

Publisher  IEEE Computer Society
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