Gaussian similarity preserving for cross-modal hashing

Lin, Liuyin; Shu, Xin

doi:10.1016/j.neucom.2022.04.125

Cited by 3 publications

(1 citation statement)

References 50 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared to unsupervised cross-modal hashing, supervised cross-modal hashing using label information can significantly increase the semantics of hash codes and thus improve the retrieval performance of hash codes [6,29,41,19,12,17,35]. Representative works include LBMCH [45] describes the semantic correspondence across schemas by bridging the gaps that exist in different hash spaces.…”

Section: Ii2 Supervised Cross-modal Hashingmentioning

confidence: 99%

Adaptive Asymmetric Label-guided Hashing for Multimedia Search

Long¹

2022

Preprint

View full text Add to dashboard Cite

With the rapid growth of multimodal media data on the Web in recent years, hash learning methods as a way to achieve efficient and flexible cross-modal retrieval of massive multimedia data have received a lot of attention from the current Web resource retrieval research community. Existing supervised hashing methods simply transform label information into pairwise similarity information to guide hash learning, leading to a potential risk of semantic error in the face of multi-label data. In addition, most existing hash optimization methods solve NP-hard optimization problems by employing approximate approximation strategies based on relaxation strategies, leading to a large quantization error. In order to address above obstacles, we present a simple yet efficient Adaptive Asymmetric Label-guided Hashing, named A2LH, for Multimedia Search. Specifically, A2LH is a two-step hashing method. In the first step, we design an association representation model between the different modality representations and semantic label representation separately, and use the semantic label representation as an intermediate bridge to solve the semantic gap existing between different modalities. In addition, we present an efficient discrete optimization algorithm for solving the quantization error problem caused by relaxation-based optimization algorithms. In the second step, we leverage the generated hash codes to learn the hash mapping functions. The experimental results show that our proposed method achieves optimal performance on all compared baseline methods.

show abstract