Title: Supervised Hashing based on Energy Minimization,
Abstract: Recently, supervised hashing methods have attracted much attention since they
can optimize retrieval speed and storage cost while preserving semantic
information. Because hashing codes learning is NP-hard, many methods resort to
some form of relaxation technique. But the performance of these methods can
easily deteriorate due to the relaxation. Luckily, many supervised hashing
formulations can be viewed as energy functions, hence solving hashing codes is
equivalent to learning marginals in the corresponding conditional random field
(CRF). By minimizing the KL divergence between a fully factorized distribution
and the Gibbs distribution of this CRF, a set of consistency equations can be
obtained, but updating them in parallel may not yield a local optimum since the
variational lower bound is not guaranteed to increase. In this paper, we use a
linear approximation of the sigmoid function to convert these consistency
equations to linear systems, which have a closed-form solution. By applying
this novel technique to two classical hashing formulations KSH and SPLH, we
obtain two new methods called EM (energy minimizing based)-KSH and EM-SPLH.
Experimental results on three datasets show the superiority of our methods.