Abstract
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 st