Unsupervised Hierarchical Dynamic Similarity Hashing for Multimedia Retrieval

Yunfei Chen, Zhan Yang, Jun Long

Abstract: Unsupervised cross-modal hashing methods have become a core technology for retrieving vast amounts of heterogeneous multimedia information due to their advantages in retrieval speed and storage efficiency. Although these methods have made significant progress in the field of multimedia retrieval, they still face challenges related to inaccurate similarity measurements and incomplete embedding of relational information. To address these issues, we propose Unsupervised Hierarchical Dynamic Similarity Hashing(UHDSH) for multimedia retrieval. First, the Semantic Similarity Measurement Layer extracts common semantic information within multimedia data to construct a dynamic fluctuation similarity hypergraph, which guides the training of the hash function. Second, the Relational Constraint Hashing Layer, based on the dynamic fluctuation similarity hypergraph embedding technique and multimodal feature reconstruction, ensures the precise embedding of both semantic and relational information. Finally, we conducted comprehensive experiments on two widely used datasets, MIR Flickr and NUS-WIDE. Our proposed UHDSH method achieves a maximum improvement of 5.06% over the best baseline methods. The code is publicly available at https://github.com/YunfeiChenMY/UHDSH.