Recently, hashing is widely-used in approximate nearest neighbor search for its storage and computational efficiency. Due to the lack of labeled data in practice, many studies focus on unsupervised hashing. 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 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 hash code learning. Moreover, most of hashing methods ignore the basic characteristics of hash codes such as collisions, which will cause instability of hash codes to disturbance. In this paper, we propose a new method named Comprehensive sImilarity Mining and cOnsistency learNing (CIMON). First, we use global constraint learning and similarity statistical distribution to obtain reliable and smooth guidance. Second, image augmentation and consistency learning will be introduced to explore both semantic and contrastive consistency to derive robust hash codes with fewer collisions. Extensive experiments on several benchmark datasets show that the proposed method consistently outperforms a wide range of state-of-the-art methods in both retrieval performance and robustness.

本文提出了一种名为CIMON的新方法，通过全局细化和相似性统计分布获得可靠且平滑的指导，并引入语义和对比一致性学习来推导既具有扰动不变性又具有区分性的哈希码，通过在多个基准数据集上进行的大量实验证明，该提出的方法在检索性能和鲁棒性方面均优于一系列现有技术方法。

CIMON: 面向高质量哈希码