Anchor-based Domain Adaptive Hashing for unsupervised image retrieval

Traditional image retrieval methods suffer from a significant performance degradation when the model is trained on the target dataset and run on another dataset. To address this issue, Domain Adaptive Retrieval (DAR) has emerged as a promising solution, specifically designed to overcome domain shifts in retrieval tasks. However, existing unsupervised DAR methods still face two primary limitations: (1) they under-explore the intrinsic structure among domains, resulting in limited generalization capabilities; and (2) the models are often too complex to be applied to large-scale datasets. To tackle these limitations, we propose a novel unsupervised DAR method named Anchor-based Domain Adaptive Hashing (ADAH). ADAH aims to exploit the commonalities among domains with the assumption that a consensus latent space exists for the source and target domains. To achieve this, an anchor-based similarity reconstruction scheme is proposed, which learns a set of domain-shared anchors and domain-specific anchor graphs, and then reconstructs the similarity matrix with these anchor graphs, thereby effectively exploiting inter- and intra-domain similarity structures. Subsequently, by treating the anchor graphs as feature embeddings, we solve the Distance-Distance Difference Minimization (DDDM) problem between them and their corresponding hash codes. This preserves the similarity structure of the similarity matrix in the hash code. Finally, a two-stage strategy is employed to derive the hash function, ensuring its effectiveness and scalability. Experimental results on four datasets demonstrate the effectiveness of the proposed method.