Deep Hashing for Compact Binary Codes Learning

CVPR 2015 Venice Erin LiongJiwen LuGang WangPierre MoulinJie Zhou

In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited... (read more)

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