  Recently, hashing is widely used in approximate nearest neighbor search for
its storage and computational efficiency. Most of the unsupervised hashing
methods learn to map images into semantic similarity-preserving hash codes by
constructing local semantic similarity structure from the pre-trained model as
the guiding information, i.e., treating each point pair similar if their
distance is small in feature space. However, due to the inefficient
representation ability of the pre-trained model, many false positives and
negatives in local semantic similarity will be introduced and lead to error
propagation during the hash code learning. Moreover, few of the methods
consider the robustness of models, which will cause instability of hash codes
to disturbance. In this paper, we propose a new method named
{\textbf{C}}omprehensive s{\textbf{I}}milarity {\textbf{M}}ining and
c{\textbf{O}}nsistency lear{\textbf{N}}ing (CIMON). First, we use global
refinement and similarity statistical distribution to obtain reliable and
smooth guidance. Second, both semantic and contrastive consistency learning are
introduced to derive both disturb-invariant and discriminative hash codes.
Extensive experiments on several benchmark datasets show that the proposed
method outperforms a wide range of state-of-the-art methods in both retrieval
performance and robustness.
