Deep Hashing Based on VAE-GAN for Efficient Similarity Retrieval

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外文摘要：Inspired by the recent advances in generative networks,we propose a VAE-GAN based hashing framework for fast image retrieval.The method combines a Variational autoencoder (VAE) with a Generative adversarial network (GAN) to generate content preserving images for pairwise hashing learning.By accepting real image and systhesized image in a pairwise form,a semantic perserving feature mapping model is learned under a adversarial generative process.Each image feature vector in the pairwise is converted to a hash codes,which are used in a pairwise ranking loss that aims to preserve relative similarities on images.Extensive experiments on several benchmark datasets demonstrate that the proposed method shows substantial improvement over the state-of-the-art hashing methods.

外文关键词：

Image retrievalLearning to hashVariational autoencoder(VAE)Generative adversarial network(GAN)

作者：

JIN Guoqing、ZHANG Yongdong、LU Ke

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作者单位：

Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

University of Chinese Academy of Sciences, Beijing 100049, China

基金：

This work is supported by the National Key Research and Development Program of ChinaNational Nature Science Foundation of ChinaNational Nature Science Foundation of ChinaNational Nature Science Foundation of ChinaNational Nature Science Foundation of ChinaNational Nature Science Foundation of China

项目编号：

2017YFB100220361525206No.61672495No.61771458No.61702479No.61571424

出版年：

2019

DOI：

10.1049/cje.2019.08.001

中国电子杂志(英文版)

CSTPCDCSCDSCIEI

ISSN：1022-4653

年,卷(期)：2019.28(6)

参考文献量31