Deep Unsupervised Hashing with Selective Semantic Mining

Most of the existing unsupervised hashing methods usually construct semantic similarity structure to guide hashing learning. However, due to the lack of filtering of useless information, some wrong guiding information in the similarity structure may damage the retrieval performance. Besides, some works adopt the framework of contrastive learning to preserve the discriminative semantic information that is more important for the hashing task. But such a training strategy may incorrectly embed some semantically similar samples far away due to the absence of manual label supervision, thus producing sub-optimal hash codes. To solve the aforementioned problems, we propose a novel method named Deep Selective Semantic Mining Hashing (DSSMH). Specifically, with the prior knowledge obtained by clustering, we select semantically correct image pairs with high confidence to alleviate the guidance of wrong information and correct sampling bias in contrastive learning. Extensive experiments demonstrate that DSSMH outperforms existing state-of-the-art methods.