Supervised Semantic-Embedded Hashing for Multimedia Retrieval

With the rapid development of multimedia technologies, the efficient retrieval of large-scale multimedia information is regarded as a critical area to research. Hashing methods have achieved superiority as an effective solution for multimedia retrieval while offering the advantages of reduced storage demands and faster retrieval speeds. However, traditional hashing methods face challenges in addressing cross-modal heterogeneity and associating modal features with label information. In this paper, we introduce a novel supervised cross-modal hashing method named Supervised Semantic-Embedded Hashing (SSEH) for Multimedia Retrieval. Firstly, we designed a Semantic-Enhanced Representation module based on label mapping, which achieved deep integration of label information with multimedia features to ensure the completeness of semantic information in the hash codes. Secondly, a Class Structure Preservation module was constructed to comprehensively extract precise semantic information and association relationships from the labels, thereby ensuring the accuracy of semantic information and association relationships in the hash codes. We conducted extensive experiments on widely recognized datasets to demonstrate our SSEH method’s efficacy and robustness. The code for our experiments is available at https://github.com/YunfeiChenMY/SSEH.