Abstract
Hash center-based deep hashing methods improve upon pairwise or triplet-based approaches by assigning fixed hash centers to each class as learning targets, thereby avoiding the inefficiency of local similarity optimization. However, random center initialization often disregards inter-class semantic relationships. While existing two-stage methods mitigate this by first refining hash centers with semantics and then training the hash function, they introduce additional complexity, computational overhead, and suboptimal performance due to stage-wise discrepancies. To address these limitations, we propose \(\textbf\{Center-Reassigned Hashing (CRH)\}\), an end-to-end framework that \(\textbf\{dynamically reassigns hash centers\}\) from a preset codebook while jointly optimizing the hash function. Unlike previous methods, CRH adapts hash centers to the data distribution \(\textbf\{without explicit center optimization phases\}\), enabling seamless integration of semantic relationships into the