Deep Hashing with Semantic Hash Centers for Image Retrieval

Deep hashing presents an effective strategy for large-scale image retrieval. Current hashing methods are generally categorized by their supervision types: point-wise, pairwise, and list-wise. Recent advancements in point-wise methods (e.g., CSQ, MDS) have significantly enhanced retrieval performance across diverse datasets by pre-assigning a hash center to each class, thereby improving the discriminability of the resultant hash codes. However, these methods employ purely data-independent algorithms for generating hash centers, overlooking the semantic connections between different classes, which, we argue, could degrade retrieval performance. To tackle this problem, this article expands on the newly emerged concept of “hash centers” to introduce “semantic hash centers,” which posits that hash centers of semantically related classes should exhibit closer Hamming distances, while those of unrelated classes should be more distant. Based on this hypothesis, we propose a three-stage framework, termed Semantic Hash Centers (SHC), to produce hash codes that preserve semantics. First, we build a classification network to detect semantic similarities between classes, and utilize a data-dependent approach to similarity calculation that can adapt to varied data distributions. Next, we develop a new optimization algorithm to generate SHC. This algorithm not only maintains semantic relatedness among hash centers but also integrates a constraint to ensure a minimum distance between them, addressing the issue of excessively proximate hash centers potentially impairing retrieval performance. Finally, we train a deep hashing network with the above generated SHC to convert each image into a binary hash code. Experiments on large-scale image retrieval across several public datasets demonstrate that SHC generates more discriminative hash codes, markedly enhancing retrieval performance. Specifically, in terms of the mAP@100, mAP@1000, and mAP@ALL metrics, SHC records average improvements of +6.24%, +6.68%, and +10.39%, respectively, over the most competitive existing methods.