Deep Cross-Modal Hashing With Hashing Functions and Unified Hash Codes Jointly Learning

Due to their high retrieval efficiency and low storage cost, cross-modal hashing methods have attracted considerable attention. Generally, compared with shallow methods, deep can achieve a more satisfactory performance by integrating feature learning hash codes optimizing into same framework. However, most existing either cannot learn unified code for the two correlated data-points of different modalities in database instance or guide feedback function procedure, enhance accuracy. To address issues above, this paper, we propose novel end-to-end Deep Cross-Modal Hashing Functions Unified Hash Codes Jointly Learning (DCHUC). Specifically, an iterative optimization algorithm, DCHUC jointly learns image-text pairs pair functions unseen query pairs. With learned be used procedure; Meanwhile, procedure. Extensive experiments on three public datasets demonstrate that proposed method outperforms state-of-the-art methods.