  In recent years, a lot of attention has been devoted to efficient nearest
neighbor search by means of similarity-preserving hashing. One of the plights
of existing hashing techniques is the intrinsic trade-off between performance
and computational complexity: while longer hash codes allow for lower false
positive rates, it is very difficult to increase the embedding dimensionality
without incurring in very high false negatives rates or prohibiting
computational costs. In this paper, we propose a way to overcome this
limitation by enforcing the hash codes to be sparse. Sparse high-dimensional
codes enjoy from the low false positive rates typical of long hashes, while
keeping the false negative rates similar to those of a shorter dense hashing
scheme with equal number of degrees of freedom. We use a tailored feed-forward
neural network for the hashing function. Extensive experimental evaluation
involving visual and multi-modal data shows the benefits of the proposed
method.
