深度哈希图像检索方法综述  

Deep Hashing image retrieval methods

作　　者：刘颖 程美[3] 王富平 李大湘 刘伟 范九伦[1,3] Liu Ying;Cheng Mei;Wang Fuping;Li Daxiang;Liu Wei;Fan Jiulun(Key Laboratory of Electronic Information Application Technology for Scene Investigation,Ministry of Public Security,Xi'an 710121,China;International Joint Research Center for Wireless Communication and Information Processing,Xi'an 710121,China;Center for Image and Information Processing,Xi'an University of Posts and Telecommunications,Xi'an 710121,China)

机构地区：[1]电子信息现场勘验应用技术公安部重点实验室,西安710121 [2]无线通信与信息处理技术国际联合研究中心,西安710121 [3]西安邮电大学图像与信息处理研究所,西安710121

出　　处：《中国图象图形学报》2020年第7期1296-1317,共22页Journal of Image and Graphics

基　　金：国家重点研发计划项目(2017YFC080380);国家自然科学基金项目(61671377,61802305);公安部科技强警项目(2016GABJC51)。

摘　　要：随着网络上图像和视频数据的快速增长,传统图像检索方法已难以高效处理海量数据。在面向大规模图像检索时,特征哈希与深度学习结合的深度哈希技术已成为发展趋势,为全面认识和理解深度哈希图像检索方法,本文对其进行梳理和综述。根据是否使用标签信息将深度哈希方法分为无监督、半监督和监督深度哈希方法,根据无监督和半监督深度哈希方法的主要研究点进一步分为基于卷积神经网络(convolutional neural networks,CNN)和基于生成对抗网络(generative adversarial networks,GAN)的无监督/半监督深度哈希方法,根据数据标签信息差异将监督深度哈希方法进一步分为基于三元组和基于成对监督信息的深度哈希方法,根据各种方法使用损失函数的不同对每类方法中一些经典方法的原理及特性进行介绍,对各种方法的优缺点进行分析。通过分析和比较各种深度哈希方法在CIFAR-10和NUS-WIDE数据集上的检索性能,以及深度哈希算法在西安邮电大学图像与信息处理研究所(Center for Image and Information Processing,CIIP)自建的两个特色数据库上的测试结果,对基于深度哈希的检索技术进行总结,分析了深度哈希的检索技术未来的发展前景。监督深度哈希的图像检索方法虽然取得了较高的检索精度。但由于监督深度哈希方法高度依赖数据标签,无监督深度哈希技术更加受到关注。基于深度哈希技术进行图像检索是实现大规模图像数据高效检索的有效方法,但存在亟待攻克的技术难点。针对实际应用需求,关于无监督深度哈希算法的研究仍需要更多关注。The efficient processing of massive amounts of data obtained as a result of the rapid growth of image and video data transmission is becoming increasingly difficult for traditional image retrieval methods.The feature-Hashing technology,which can achieve efficient feature compression and fast feature matching and image retrieval,is introduced to address this issue.The deep learning technology also has unique advantages in feature extraction and compact description.The deep Hashing technology,which combines feature Hashing with deep learning,has become an interesting research topic in the area of large-scale image retrieval in solving the problem of large-scale image retrieval.Image retrieval methods based on deep Hashing have attracted increasing attention.Extensive research on image retrieval technologies using deep Hashing has been conducted in recent years and is reported in this paper.First,the deep Hashing method is divided into unsupervised,semisupervised,and supervised deep Hashing methods according to whether label information is used.Second,unsupervised and semisupervised deep Hashing methods are further divided into two types,namely,unsupervised/semisupervised deep Hashing based on deep network models and GANs(generative adversarial networks).In the unsupervised deep Hashing based on the deep network models,the Deep Bit algorithm and the SADH(similarity-adaptive deep Hashing)algorithm are mainly introduced.In the GAN-based unsupervised deep Hashing method,we illustrate the principles of Hash GAN,BGAN(binary generative adversarial networks)and PGH(progressive generative Hashing)algorithms.In the semi-supervised deep Hashing method,the SSDH(semi-supervised discriminant Hashing)algorithm based on the depth models and the SSGAH(semi-supervised generative adversarial Hashing)algorithm based on the generated adversarial network are mainly interpreted.Third,the supervised deep Hashing algorithms are divided into deep Hashing methods based on triple labels and data pairs depending on the different types of label

关 键 词：图像检索 无监督 监督 深度学习 哈希 深度哈希 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]