BH2I-GAN: Bidirectional Hash_code-to-Image Translation using Multi-Generative Multi-Adversarial Nets

Highlights：

• 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.

摘要

•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.