  We propose a flexible procedure for large-scale image search by hash
functions with kernels. Our method treats binary codes and pairwise semantic
similarity as latent and observed variables, respectively, in a probabilistic
model based on Gaussian processes for binary classification. We present an
efficient inference algorithm with the sparse pseudo-input Gaussian process
(SPGP) model and parallelization. Experiments on three large-scale image
dataset demonstrate the effectiveness of the proposed hashing method, Gaussian
Process Hashing (GPH), for short binary codes and the datasets without
predefined classes in comparison to the state-of-the-art supervised hashing
methods.
