Deep Hashing with Semantic Hash Centers for Image Retrieval
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
Deep hashing is an effective approach for large-scale image retrieval.
Current methods are typically classified by their supervision types:
point-wise, pair-wise, and list-wise. Recent point-wise techniques (e.g., CSQ,
MDS) have improved retrieval performance by pre-assigning a hash center to each
class, enhancing the discriminability of hash codes across various datasets.
However, these methods rely on data-independent algorithms to generate hash
centers, which neglect the semantic relationships between classes and may
degrade retrieval performance.
This paper introduces the concept of semantic hash centers, building on the
idea of traditional hash centers. We hypothesize that hash centers of
semantically related classes should have closer Hamming distances, while those
of unrelated classes should be more distant. To this end, we propose a
three-stage framework, SHC, to generate hash codes that preserve semantic
structure.
First, we develop a classification network to identify semantic similarities
between classes using a data-dependent similarity calculation that adapts to
varying data distributions. Second, we introduce an optimization algorithm to
generate semantic hash centers, preserving semantic relatedness while enforcing
a minimum distance between centers to avoid excessively similar hash codes.
Finally, a deep hashing network is trained using these semantic centers to
convert images into binary hash codes.
Experimental results on large-scale retrieval tasks across several public
datasets show that SHC significantly improves retrieval performance.
Specifically, SHC achieves average improvements of +7.26%, +7.62%, and +11.71%
in MAP@100, MAP@1000, and MAP@ALL metrics, respectively, over state-of-the-art
methods.
