Unsupervised Online Cross-modal Hashing With Multiple Association Exploitation

Unsupervised online cross-modal hashing has gained increasing attention for its effectiveness in streaming data retrieval. However, existing methods primarily focus on exploiting shared properties, overlooking semantic shifts among chunks and specific properties of each modality. To address these challenges, we propose a novel method called Unsupervised Online Cross-Modal Hashing with multiple association exploitation, UOCMH in short. Specifically, we design a hierarchical matrix factorization framework. It skillfully constructs robust orthogonal bases, multi-modality specific representations, and unified common representations, thereby capturing semantic associations among multi-modality streaming data more sufficiently. Additionally, we present a semantic auto-encoder scheme as hash functions. It builds the association between features and hash codes, facilitating the stability of the hashing process. Extensive experiments on the widely-used benchmark datasets demonstrate the superiority of the proposed UOCMH.