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收稿：2019-10-23，
修回：2020-1-11，
录用：2020-1-18，
纸质出版：2020-07-16
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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 DeepBit 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 HashGAN
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 information used. Designing loss functions and controlling quantization errors occupies important parts of deep Hashing image retrieval
hence the algorithms are classified in more detail according to different loss functions in several supervised deep Hashing methods. In the deep Hashing methods based on paired supervision information
the algorithm are further classified as deep Hashing methods using square loss function
using cross-entropy loss function
or designing a new loss function. 1) In the Hash method using the square loss function
CNNH (convolutional neural network Hashing) is introduced in detail. 2) In the Hash method using the cross entropy loss function
we mainly describe DPSH (deep supervised Hashing with pairwise labels)
DSDH (deep supervised discrete Hashing)
HashNet and HashGAN four algorithm models. 3) DSH (deep supervised Hashing) and DVSQ (deep visual-semantic quantization) algorithms design new loss functions in their research. Among the deep Hashing methods based on triple labels
1) deep Hashing methods using triple loss function are mainly illustrated: NINH (network in network Hashing)
DRSCH (deep regularized similarity comparison Hashing)
DTQ (deep triplet quantization). And the triple loss function is actually improved from the hinge loss function; 2) the deep Hashing methods using the triple entropy loss function: DTSH (deep supervised Hashing with triplet). Because triple labels require a lot of image preprocessing
there is little research about it. After introducing principles and characteristics of selected classical algorithms
and the advantages and disadvantages of each deep supervised algorithm are analyzed. Fourth
we compare the retrieval performances of each algorithm on two commonly used large-scale datasets
namely
CIFAR-10 and NUS-WIDE. We also investigate the performance of the DPSH algorithm on two specialized datasets
namely
CⅡP(Center for Image and Information Process-ing)-CSID(crime scene investigation image database) and CⅡP-TPID(tread pattern image dataset)
and summarize existing deep Hashing-based retrieval technologies. Finally
we discuss the future development of deep Hashing-based retrieval algorithms. Hashing has improved the image retrieval speed on very-large-scale datasets
but the overall retrieval performance remains low. Hashing with deep learning has been extensively used in recent years to extract features of high-level semantic information. The CNNH algorithm is the first of such attempts. The excellent performance of CNNH has opened a new chapter for Hashing-based image retrieval methods. Deep Hashing methods based on paired supervisory information or triplet supervised information have caused improvements in algorithm structure
Hashing function
loss function
and control quantization error. However
the improvement of triple-based deep Hashing methods is limited by their requirement of numerous image-preprocessing works. Deep Hashing methods based on pairwise label information provide some insights into the way to enhance triple-based deep Hashing methods. For example
NINH improves the network structure from CNNH
and the DTSH algorithm is based on the algorithm structure of DPSH. Deep Hashing-based image retrieval methods have their own advantages in retrieval performance. The existing methods have achieved superior retrieval precision
but a space for improvement in controlling quantization error and learning image representation remains. Labeling images one by one will require high labor and time costs because the supervised deep Hashing method highly depends on data labels but the data scale in reality is expanding. Scholars have paid increasing attention to unsupervised deep Hashing technologies and achieved significant performance improvements by combining such technologies with GANs or deep mod-els. Experimental results on two special databases show that the DPSH algorithm performs efficiently on CⅡP-CSID and competitively on CⅡP-TPID. The deep Hashing technology is an effective method for large-scale image retrieval
but major problems remain unsolved. On the basis of the needs of practical applications
the research on unsupervised deep Hashing algorithms requires further attention. Network models and feature learning should also be improved in different ways depending on dataset characteristics and case used. The potential applications of the deep Hashing technology are wide
including biometrics and multimodal retrieval. The experimental results of the DPSH algorithm on two special databases reveal the need to customize network models and feature-learning algorithms in accordance with their cases used. Such a need renders the deep Hashing technology for critical image retrieval research areas and presents a great potential for various specialized industries.
Andoni A and Indyk P. 2006. Near-optimal Hashing algorithms for approximate nearest neighbor in high dimensions//Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science. Berkeley, CA, USA: IEEE: 459-468[ DOI: 10.1109/FOCS.2006.49 http://dx.doi.org/10.1109/FOCS.2006.49 ]
Atrey P K, Hossain M A, El Saddik A and Kankanhalli M S. 2010. Multimodal fusion for multimedia analysis:a survey. Multimedia Systems, 16(6):345-379[DOI:10.1007/s00530-010-0182-0]
Bronstein M M, Bruna J, LeCun Y, Szlam A and Vandergheynst P. 2017. Geometric deep learning:going beyond Euclidean data. IEEE Signal Processing Magazine, 34(4):18-42[DOI:10.1109/MSP.2017.2693418]
Burges C J. 1998. A tutorial on support vector machine for pattern recognition. Dta Mining and Knowledge Discovery, 2(2):121-167[DOI:10.1023/a:1009715923555]
Cao Y, Liu B, Long M S and Wang J M. 2018a. HashGAN: deep learning to Hash with pair conditional wasserstein GAN//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE: 1287-1296[ DOI: 10.1109/CVPR.2018.00140 http://dx.doi.org/10.1109/CVPR.2018.00140 ]
Cao Y, Long M S, Liu B and Wang J M. 2018b. Deep cauchy Hashing for Hamming space retrieval//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE: 1229-1237[ DOI: 10.1109/CVPR.2018.00134 http://dx.doi.org/10.1109/CVPR.2018.00134 ]
Cao Y, Long M S, Wang J M and Liu S C. 2017a. Deep visual-semantic quantization for efficient image retrieval//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE: 1328-1337[ DOI: 10.1109/CVPR.2017.104 http://dx.doi.org/10.1109/CVPR.2017.104 ]
Cao Z J, Long M S, Wang J M and Yu P S. 2017b. HashNet:deep learning to Hash by continuation//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy:IEEE, 5608-5617[DOI:10.1109/ICCV.2017.598]
Cao Y, Long M S, Wang J M, Zhu H and Wen Q F. 2016. Deep quantization network for efficient image retrieval//Proceedings of the 13th AAAI Conference on Artificial Intelligence. Phoenix, Menlo Park, CA: AAAI: 3457-3463[ DOI: 10.1109/cvpr.2017.104 http://dx.doi.org/10.1109/cvpr.2017.104 ]
Chatfield K, Simonyan K, Vedaldi A and Zisserman A. 2014. Return of the devil in the details: delving deep into convolutional nets//Proceedings of 2014 British Machine Vision Conference. Nottingham, UK: BMVA Press: 1-11[ DOI: 10.5244/C.28.6 http://dx.doi.org/10.5244/C.28.6 ]
Chen Z X, Yuan X, Lu J W, Tian Q and Zhou J. 2018. Deep Hashing via discrepancy minimization//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE: 6838-6847[ DOI: 10.1109/CVPR.2018.00715 http://dx.doi.org/10.1109/CVPR.2018.00715 ]
Chua T S, Tang J H, Hong R C, Li H J, Luo Z P and Zheng Y T. 2009. NUS-WIDE: a real-world web image database from national university of Singapore//Proceedings of the ACM International Conference on Image and Video Retrieval. Santorini, Fira, Greece: ACM: 48-49[ DOI: 10.1145/1646396.1646452 http://dx.doi.org/10.1145/1646396.1646452 ]
CIIP-CSID. 2018. CⅡP-CSID[DB/OL].[2019-08-27] . http://www.xuptciip.com.cn/show.html?database-xksjk04 http://www.xuptciip.com.cn/show.html?database-xksjk04
西安邮电大学图像处理团队现勘图像数据库. 2018.西安邮电大学图像处理团队现勘图像数据库[DB/OL].[2019-08-27] . http://www.xuptciip.com.cn/show.html?database-xksjk04 http://www.xuptciip.com.cn/show.html?database-xksjk04
CIIP-TPID. 2018. CⅡP-TPID[DB/OL].[2019-08-27] . http://www.xuptciip.com.cn/show.html?database-xksjk03 http://www.xuptciip.com.cn/show.html?database-xksjk03
西安邮电大学图像处理团队轮胎表面花纹图像数据库. 2018.西安邮电大学图像处理团队轮胎表面花纹图像数据库[DB/OL].[2019-08-27] . http://www.xuptciip.com.cn/show.html?database-xksjk03 http://www.xuptciip.com.cn/show.html?database-xksjk03
Dalal N and Triggs B. 2005. Histograms of oriented gradients for human detection//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE: 886-893[ DOI: 10.1109/CVPR.2005.177 http://dx.doi.org/10.1109/CVPR.2005.177 ]
Deng C, Chen Z J, Liu X L, Gao X B and Tao D C. 2018. Triplet-based deep Hashing network for cross-modal retrieval. IEEE Transactions on Image Processing, 27(8):3893-3903[DOI:10.1109/tip.2018.2821921]
Dizaji K G, Zheng F, Nourabadi S N, Yang Y H, Deng C and Huang H. 2018. Unsupervised deep generative adversarial Hashing network//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE: 3664-3673[ DOI: 10.1109/CVPR.2018.00386 http://dx.doi.org/10.1109/CVPR.2018.00386 ]
Friedman J H. 1997. On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1(1):55-77[DOI:10.1023/a:1009778005914]
Gong Y C, Lazebnik S, Gordo A and Perronnin F. 2013. Iterative quantization:a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12):2916-2929[DOI:10.1109/TPAMI.2012.193]
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge, MA: MIT: 2672-2680
Gray R M and Neuhoff D L. 1998. Quantization. IEEE Transactions on Information Theory, 44(6):2325-2383[DOI:10.1109/18.720541]
Hadsell R, Chopra S and Lecun Y. 2006. Dimensionality reduction by learning an invariant mapping//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE: 1735-1742[ DOI: 10.1109/CVPR.2006.100 http://dx.doi.org/10.1109/CVPR.2006.100 ]
He K M, Wen F and Sun J. 2013. K-means Hashing: an affinity-preserving quantization method for learning binary compact codes//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland: IEEE: 2938-2945[ DOI: 10.1109/CVPR.2013.378 http://dx.doi.org/10.1109/CVPR.2013.378 ]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778[ DOI: 10.1109/CVPR.2016.90 http://dx.doi.org/10.1109/CVPR.2016.90 ]
Hinton G E and Salakhutdinov R R. 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507[DOI:10.1126/science.1127647]
Jégou H, Douze M and Schmid C. 2011. Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1):117-128[DOI:10.1109/TPAMI.2010.57]
Jiang Q Y and Li W J. 2017. Deep cross-modal Hashing//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE: 3270-3278[ DOI: 10.1109/CVPR.2017.348 http://dx.doi.org/10.1109/CVPR.2017.348 ]
Kim S and Choi S. 2011. Semi-supervised discriminant Hashing//Proceedings of the 11th IEEE International Conference on Data Mining. Vancouver: IEEE: 1122-1127[ DOI: 10.1109/ICDM.2011.128 http://dx.doi.org/10.1109/ICDM.2011.128 ]
Kipf T and Welling M. 2017. Semi-supervised classification with graph convolutional networks//Proceedings of the 5th International Conference on Learning Representations, Toulon, France: [s.n.]: 1-14
Kong W H and Li W J. 2012. Isotropic Hashing//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, NY, USA: Curran Associates Inc.: 1646-1654
Krizhevsky A. 2009. Learning Multiple Layers of Features from Tiny Images. Technical Report TR-2009. University of Toronto
Lai H J, Pan Y, Liu Y and Yan S C. 2015. Simultaneous feature learning and Hash coding with deep neural networks//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA: IEEE: 3270-3278[ DOI: 10.1109/CVPR.2015.7298947 http://dx.doi.org/10.1109/CVPR.2015.7298947 ]
Li Q, Sun Z N, He R and Tan T N. 2017. Deep supervised discrete Hashing//Proceedings of the 22nd Advances in Neural Information Processing Systems. Cambridge, MA: NIPS: 2482-2491
Li W J and Zhou Z H. 2015. Learning to Hash for big data:current status and future trends. Chinese Science Bulletin, 60(5/6):485-490
李武军, 周志华. 2015.大数据哈希学习:现状与趋势.科学通报, 60(5/6):485-490)[DOI:10.1360/N972014-00841]
Li W J, Wang S and Kang W C. 2016. Feature learning based deep supervised Hashing with pairwise labels//Proceedings of the 25th International Joint Conference on Artificial Intelligence. Menlo Park, CA: AAAI: 1711-1717
Lin K, Lu J W, Chen C S and Zhou J. 2016. Learning compact binary descriptors with unsupervised deep neural networks//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 1183-1192[ DOI: 10.1109/CVPR.2016.133 http://dx.doi.org/10.1109/CVPR.2016.133 ]
Lin K, Yang H F, Hsiao J H and Chen C S. 2015. Deep learning of binary Hash codes for fast image retrieval//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Boston, MA: IEEE: 27-35[ DOI: 10.1109/CVPRW.2015.7301269 http://dx.doi.org/10.1109/CVPRW.2015.7301269 ]
Liu B, Cao Y, Long M S, Wang J M and Wang J D. 2018. Deep triplet quantization//Proceedings of the 26th ACM International Conference on Multimedia. New York: ACM: 755-763
Liu H M, Wang R P, Shan S G and Chen X L. 2016. Deep supervised Hashing for fast image retrieval//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 2064-2072[ DOI: 10.1109/CVPR.2016.227 http://dx.doi.org/10.1109/CVPR.2016.227 ]
Liu L, Yu M Y and Shao L. 2017. Latent structure preserving Hashing. International Journal of Computer Vision, 122(3):439-457[DOI:10.1007/s11263-016-0931-4]
Liu W, Wang J, Ji R R, Jiang Y G and Chang S F. 2012. Supervised Hashing with kernels//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, Rhode Island, USA: IEEE: 2074-2081[ DOI: 10.1109/CVPR.2012.6247912 http://dx.doi.org/10.1109/CVPR.2012.6247912 ]
Liu Y, Hu D and Fan J L. 2018. A survey of crime scene investigation image retrieval. Acta Electronica Sinica, 46(3):761-768
刘颖, 胡丹, 范九伦. 2018.现勘图像检索综述.电子学报, 46(3):761-768)[DOI:10.3969/j.issn.0372-2112.2018.03.035]
Liu Y, Zhang S and Fan J L. 2019. Tread pattern image classification with feature fusion based on transfer learning. Computer Engineering and Design, 40(5):1401-1406
刘颖, 张帅, 范九伦. 2019.基于迁移学习及特征融合的轮胎花纹图像分类.计算机工程与设计, 40(5):1401-1406)[DOI:10.16208/j.issn1000-7024.2019.05.037]
Lowe D G. 1999. Object recognition from local scale-invariant features//Proceedings of the 7th IEEE International Conference on Computer Vision. Kerkyra, Greece: ICCV: 1150-1151[ DOI: 10.1109/ICCV.1999.790410 http://dx.doi.org/10.1109/ICCV.1999.790410 ]
Ma Y Q, He Y, Ding F, Hu S, Li J and Liu X L. 2018. Progressive generative Hashing for image retrieval//Proceedings of the 27th International Joint Conference on Artificial Intelligence. San Mateo, CA: IJCAI: 871-877[ DOI: 10.24963/ijcai.2018/121 http://dx.doi.org/10.24963/ijcai.2018/121 ]
Meyer-Bäse A, Jancke K, Wismüller A and Georgiopoulos M. 2004. Fast k-dimensional tree-structured vector quantization encoding method for image compression. Optical Engineering, 43(5):1012-1013[DOI:10.1117/1.1683885]
Norouzi M and Fleet D J. 2011. Minimal loss Hashing for compact binary codes//Proceedings of the 28th International Conference on Machine Learning. Bellevue, Washington, USA: ICML: 353-360
Norouzi M, Fleet D J and Salakhutdinov R. 2012. Hamming distance metric learning//Proceedings of the 25th International Conference on Neural Information Processing Systems. Cambridge, MA: NIPS: 1061-1069
Ojala T, Pietikäinen M and Mäenpää T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987[DOI:10.1109/TPAMI.2002.1017623]
Oliva A and Torralba A. 2001. Modeling the shape of the scene:a holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3):145-175[DOI:10.1023/a:1011139631724]
Shen F M, Shen C H, Liu W and Shen H T. 2015. Supervised discrete Hashing//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA: IEEE: 37-45[ DOI: 10.1109/CVPR.2015.7298598 http://dx.doi.org/10.1109/CVPR.2015.7298598 ]
Shen F M, Xu Y, Liu L, Yang Y, Huang Z and Shen H T. 2018. Unsupervised deep Hashing with similarity-adaptive and discrete optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12):3034-3044[DOI:10.1109/TPAMI.2018.2789887]
Simonyan K and Zisserman. 2014. A very deep convolutional networks for large-scale image recognition//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, IEEE: 1-14
Song J K, He T, Gao L L, Xu X, Hanjalic A and Shen T H. 2018. Binary generative adversarial networks for image retrieval//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI: 394-401
Sun Y. 2018. The research and development of deep learning Hashing. Journal of Data Communication, (2):49-54
孙瑶. 2018.深度学习哈希研究与发展.数据通信, (2):49-54
Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE: 1-9[ DOI: 10.1109/CVPR.2015.7298594 http://dx.doi.org/10.1109/CVPR.2015.7298594 ]
Wang B K, Yang Y, Xu X, Hanjalic A and Shen H T. 2017. Adversarial cross-modal retrieval//Proceedings of the 25th ACM International Conference on Multimedia. New York: ACM: 154-162[ DOI: 10.1145/3123266.3123326 http://dx.doi.org/10.1145/3123266.3123326 ]
Wang D X, Cui P, Ou M D and Zhu W W. 2015. Learning compact Hash codes for multimodal representations using orthogonal deep structure. IEEE Transactions on Multimedia, 17(9):1404-1416[DOI:10.1109/tmm.2015.2455415]
Wang G A, Hu Q H, Cheng J and Hou Z G. 2018. Semi-supervised generative adversarial Hashing for image retrieval//Proceedings of the 15th European Conference on Computer Vision (ECCV). Munich: Springer: 469-485[ DOI: 10.1007/978-3-030-01267-0_29 http://dx.doi.org/10.1007/978-3-030-01267-0_29 ]
Wang J, Kumar S and Chang S F. 2010. Semi-supervised Hashing for scalable image retrieval//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE: 3424-3431[ DOI: 10.1109/CVPR.2010.5539994 http://dx.doi.org/10.1109/CVPR.2010.5539994 ]
Wang X F, Shi Y and Kitani K M. 2016. Deep supervised Hashing with triplet labels//Proceedings of the 13th Asian Conference on Computer Vision. Taipei, China: Springer: 70-84[ DOI: 10.1007/978-3-319-54181-5_5 http://dx.doi.org/10.1007/978-3-319-54181-5_5 ]
Wei E M and Ozdaglar A. 2012. Distributed alternating direction method of multipliers//Proceedings of the 51st IEEE Conference on Decision and Control (CDC). Maui: IEEE: 5445-5450[ DOI: 10.1109/CDC.2012.6425904 http://dx.doi.org/10.1109/CDC.2012.6425904 ]
Weiss Y, Torralba A and Fergus R. 2008. Spectral Hashing//Proceedings of the 21st International Conference on Neural Information Processing Systems.Vancouver, British Columbia, Canada: NIPS: 1753-1760
Wu C X, Zhu J K, Cai D, Chen C and Bu J J. 2013. Semi-supervised nonlinear Hashing using bootstrap sequential projection learning. IEEE Transactions on Knowledge and Data Engineering, 25(6):1380-1393[DOI:10.1109/tkde.2012.76]
Xia R K, Pan Y, Lai H J, Liu C and Yan S C. 2014. Supervised Hashing for image retrieval via image representation learning//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada: Menlo Park, CA: AAAI: 2156-2162
Yang A B and Shou Z Y. 2018. A survey of sorting Hash algorithms in image retrieval. Journal of Data Communication, (6):34-40
杨安邦, 寿震宇. 2018.图像检索中的排序哈希算法研究综述.数据通信, (6):34-40
Yang M and Chen N. 2018. Cover song identification based on fusion of deep learning and manual design features. Journal of East China University of Science and Technology (Natural Science Edition), 44(5):752-759
杨妹, 陈宁. 2018.基于深度学习和手工设计特征融合的翻唱歌曲识别模型.华东理工大学学报(自然科学版), 44(5):752-759)[DOI:10.14135/j.cnki.1006-3080.20170704003]
Yang Y, Shen F M, Shen H T, Li H X and Li X L. 2015. Robust discrete spectral Hashing for large-scale image semantic indexing. IEEE Transactions on Big Data, 1(4):162-171[DOI:10.1109/TBDATA.2016.2516024]
Yu S X and Shi J. 2003. Multiclass spectral clustering//Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France: IEEE: #313
Zhang J and Peng Y X. 2019. SSDH:semi-supervised deep Hashing for large scale image retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 29(1):212-225[DOI:10.1109/TCSVT.2017.2771332]
Zhang P C, Zhang W, Li W J and Guo M Y. 2014. Supervised Hashing with latent factor models//Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. Gold Coast, Queensland, Australia: SIGIR: 173-182[ DOI: 10.1145/2600428.2609600 http://dx.doi.org/10.1145/2600428.2609600 ]
Zhang R M, Lin L, Zhang R, Zuo W M and Zhang L. 2015. Bit-scalable deep Hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Transactions on Image Processing, 24(12):4766-4779[DOI:10.1109/TIP.2015.2467315]
Zheng L, Yang Y and Tian Q. 2018. SIFT meets CNN:a decade survey of instance retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(5):1224-1244[DOI:10.1109/TPAMI.2017.2709749]
Zhou X, Shen F M, Liu L, Liu W, Nie L Q, Yang Y and Shen H T. 2018. Graph convolutional network Hashing. IEEE Transactions on Cybernetics, 50(4):1460-1472[DOI:10.1109/TCYB.2018.2883970]
Zhu H, Long M S, Wang J M and Cao Y. 2016. Deep Hashing network for efficient similarity retrieval//Proceedings of the 13th AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI: 2415-2421
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