Title
BH2I-GAN: Bidirectional Hash_code-to-Image Translation using Multi-Generative Multi-Adversarial Nets
Abstract
•We achieve effective deep hash retrieval by mapping and reversely mapping feature in multiple-GANs framework to simultaneously reduce storage cost truly and to obtain satisfactory user acceptance on the basis of acceptable retrieval precision.•We propose supervised manifold similarity to obtain better retrieval performance including retrieval precision and user acceptance followed by detailed demonstration.•We prove that Poisson distribution induced by tremendous hash codes can be initialized as generative distribution to fit real distribution. As an extension, any additive distribution can be utilized to initialize generative distribution to fit real distribution.•Experiments show that BH2I-GAN yields competitive retrieval performance comparing with state-of-the-art hashing methods, and obtains significant storage reduction as well as high-quality reconstruction from hash code. Besides, all retrieved images locate in the neighborhood of queries, which makes satisfactory user acceptance.
Year
DOI
Venue
2023
10.1016/j.patcog.2022.109010
Pattern Recognition
Keywords
DocType
Volume
Deep hashing,Generative adversarial nets,Low storage cost,Hash_code-to-image,Supervised manifold similarity
Journal
133
ISSN
Citations 
PageRank 
0031-3203
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Liming Xu100.68
Xianhua Zeng2113.84
Weisheng Li33719.68
Yicai Xie401.69