Multi-Granularity Semantic Complementarity Fusion Hashing for Cross-Modal Retrieval

Hashing techniques have attracted significant attention in cross-modal retrieval tasks due to their high query efficiency and low storage overhead. However, some methods map heterogeneous features and labels into the same semantic space, ignoring their complementary relationship, which leads to semantic loss. When leveraging pairwise similarity relationships, the affinity matrix incurs high computational costs, and the asymmetric proxy strategy lacks sufficient semantic degrading retrieval performance. Additionally, optimizing binary hash codes remains a challenging task. A novel algorithm, Multi-granularity Semantic Complementarity Fusion Hashing (MSCFH), is proposed to address these issues. First, it learns multi-granularity semantic representations from features and labels, then complements their semantics through a complementarity fusion strategy. Subsequently, an asymmetric semantic learning framework is constructed, guided by label similarities, to learn discriminative hash codes. Furthermore, an efficient discrete optimization algorithm is introduced to update the hash codes. Extensive experiments on three benchmark datasets show that the proposed MSCFH demonstrates the best retrieval accuracy and highly competitive training efficiency compared to state-of-the-art approaches.