Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing

Abstract

Unsupervised cross-modal hashing is a topic of considerable interest due to its advantages in terms of low storage costs and fast retrieval speed. Despite the impressive achievements of existing solutions, two challenges remain unaddressed: (1) Semantic similarity obtained without supervision is not accurate enough, and (2) the preservation of similarity structures lacks effectiveness due to the neglect of both global and local similarity. This paper introduces a new method, Multi-Grained Similarity Preserving and Updating (MGSPU), to tackle these challenges. To overcome the first challenge, MGSPU employs a newly designed strategy to update the semantic similarity matrix, effectively generating a high-confidence similarity matrix by eliminating noise in the original cross-modal features. For the second challenge, a novel multi-grained similarity preserving method is proposed, aiming to enhance cross-modal hash code learning by learning consistency in multi-grained similarity structures. Comprehensive experiments on two widely used datasets with nine state-of-the-art competitors validate the superior performance of our method in cross-modal hashing.