  Unsupervised cross-modal hashing (UCMH) has become a hot topic recently.
Current UCMH focuses on exploring data similarities. However, current UCMH
methods calculate the similarity between two data, mainly relying on the two
data's cross-modal features. These methods suffer from inaccurate similarity
problems that result in a suboptimal retrieval Hamming space, because the
cross-modal features between the data are not sufficient to describe the
complex data relationships, such as situations where two data have different
feature representations but share the inherent concepts. In this paper, we
devise a deep graph-neighbor coherence preserving network (DGCPN).
Specifically, DGCPN stems from graph models and explores graph-neighbor
coherence by consolidating the information between data and their neighbors.
DGCPN regulates comprehensive similarity preserving losses by exploiting three
types of data similarities (i.e., the graph-neighbor coherence, the coexistent
similarity, and the intra- and inter-modality consistency) and designs a
half-real and half-binary optimization strategy to reduce the quantization
errors during hashing. Essentially, DGCPN addresses the inaccurate similarity
problem by exploring and exploiting the data's intrinsic relationships in a
graph. We conduct extensive experiments on three public UCMH datasets. The
experimental results demonstrate the superiority of DGCPN, e.g., by improving
the mean average precision from 0.722 to 0.751 on MIRFlickr-25K using 64-bit
hashing codes to retrieve texts from images. We will release the source code
package and the trained model on https://github.com/Atmegal/DGCPN.
