Characteristics Matching Based Hash Codes Generation for Efficient Fine-grained Image Retrieval

The rapidly growing scale of data in practice poses demands on the efficiency of retrieval models. However, for fine-grained image retrieval task, there are inherent contradictions in the design of hashing based efficient models. Firstly, the limited information embedding capacity of low-dimensional binary hash codes, coupled with the detailed information required to describe fine-grained categories, results in a contradiction in feature learning. Secondly, there is also a contradiction between the complexity of fine-grained feature extraction models and retrieval efficiency. To address these issues, in this paper, we propose the characteristics matching based hash codes generation method. Coupled with the cross-layer semantic information transfer module and the multi-region feature embedding module, the proposed method can generate hash codes that effectively capture fine-grained differences among samples while ensuring efficient inference. Extensive experiments on widely-used datasets demonstrate that our method can significantly outperform state-of-the-art methods.