  Hashing methods have been widely used for efficient similarity retrieval on
large scale image database. Traditional hashing methods learn hash functions to
generate binary codes from hand-crafted features, which achieve limited
accuracy since the hand-crafted features cannot optimally represent the image
content and preserve the semantic similarity. Recently, several deep hashing
methods have shown better performance because the deep architectures generate
more discriminative feature representations. However, these deep hashing
methods are mainly designed for supervised scenarios, which only exploit the
semantic similarity information, but ignore the underlying data structures. In
this paper, we propose the semi-supervised deep hashing (SSDH) approach, to
perform more effective hash function learning by simultaneously preserving
semantic similarity and underlying data structures. The main contributions are
as follows: (1) We propose a semi-supervised loss to jointly minimize the
empirical error on labeled data, as well as the embedding error on both labeled
and unlabeled data, which can preserve the semantic similarity and capture the
meaningful neighbors on the underlying data structures for effective hashing.
(2) A semi-supervised deep hashing network is designed to extensively exploit
both labeled and unlabeled data, in which we propose an online graph
construction method to benefit from the evolving deep features during training
to better capture semantic neighbors. To the best of our knowledge, the
proposed deep network is the first deep hashing method that can perform hash
code learning and feature learning simultaneously in a semi-supervised fashion.
Experimental results on 5 widely-used datasets show that our proposed approach
outperforms the state-of-the-art hashing methods.
