Correlation embedding semantic-enhanced hashing for multimedia retrieval

Abstract

Due to its feature extraction and information processing advantages, deep hashing has achieved significant success in multimedia retrieval. Currently, mainstream unsupervised multimedia hashing methods do not incorporate associative relationship information as part of the original features in generating hash codes, and their similarity measurements do not consider the transitivity of similarity. To address these challenges, we propose the Correlation Embedding Semantic-Enhanced Hashing (CESEH) for multimedia retrieval, which primarily consists of a semantic-enhanced similarity construction module and a correlation embedding hashing module. First, the semantic-enhanced similarity construction module generates a semantically enriched similarity matrix by thoroughly exploring similarity adjacency relationships and deep semantic associations within the original data. Next, the correlation embedding hashing module integrates semantic-enhanced similarity information with intra-modal semantic information, achieves precise correlation embedding and preserves semantic information integrity. Extensive experiments on three widely-used datasets demonstrate that the CESEH method outperforms state-of-the-art unsupervised hashing methods in both retrieval accuracy and robustness. The code is available at https://github.com/YunfeiChenMY/CESEH.