  Learning based hashing plays a pivotal role in large-scale visual search.
However, most existing hashing algorithms tend to learn shallow models that do
not seek representative binary codes. In this paper, we propose a novel hashing
approach based on unsupervised deep learning to hierarchically transform
features into hash codes. Within the heterogeneous deep hashing framework, the
autoencoder layers with specific constraints are considered to model the
nonlinear mapping between features and binary codes. Then, a Restricted
Boltzmann Machine (RBM) layer with constraints is utilized to reduce the
dimension in the hamming space. Extensive experiments on the problem of visual
search demonstrate the competitiveness of our proposed approach compared to
state-of-the-art.
