  Hashing technology has been widely used in image retrieval due to its
computational and storage efficiency. Recently, deep unsupervised hashing
methods have attracted increasing attention due to the high cost of human
annotations in the real world and the superiority of deep learning technology.
However, most deep unsupervised hashing methods usually pre-compute a
similarity matrix to model the pairwise relationship in the pre-trained feature
space. Then this similarity matrix would be used to guide hash learning, in
which most of the data pairs are treated equivalently. The above process is
confronted with the following defects: 1) The pre-computed similarity matrix is
inalterable and disconnected from the hash learning process, which cannot
explore the underlying semantic information. 2) The informative data pairs may
be buried by the large number of less-informative data pairs. To solve the
aforementioned problems, we propose a Deep Self-Adaptive Hashing (DSAH) model
to adaptively capture the semantic information with two special designs:
Adaptive Neighbor Discovery (AND) and Pairwise Information Content (PIC).
Firstly, we adopt the AND to initially construct a neighborhood-based
similarity matrix, and then refine this initial similarity matrix with a novel
update strategy to further investigate the semantic structure behind the
learned representation. Secondly, we measure the priorities of data pairs with
PIC and assign adaptive weights to them, which is relies on the assumption that
more dissimilar data pairs contain more discriminative information for hash
learning. Extensive experiments on several datasets demonstrate that the above
two technologies facilitate the deep hashing model to achieve superior
performance.
