Deep Supervised Hashing for Fast Image Retrieval
Abstract
In this paper, we present a new hashing method to learn compact binary codes for highly efficient image retrieval on large-scale datasets. While the complex image appearance variations still pose a great challenge to reliable retrieval, in light of the recent progress of Convolutional Neural Networks (CNNs) in learning robust image representation on various vision tasks, this paper proposes a novel Deep Supervised Hashing (DSH) method to learn compact similarity-preserving binary code for the huge body of image data. Specifically, we devise a CNN architecture that takes pairs of images (similar/dissimilar) as training inputs and encourages the output of each image to approximate discrete values (e.g. +1/-1). To this end, a loss function is elaborately designed to maximize the discriminability of the output space by encoding the supervised information from the input image pairs, and simultaneously imposing regularization on the real-valued outputs to approximate the desired discrete values. For image retrieval, new-coming query images can be easily encoded by propagating through the network and then quantizing the network outputs to binary codes representation. Extensive experiments on two large scale datasets CIFAR-10 and NUS-WIDE show the promising performance of our method compared with the state-of-the-arts.
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CNMC, IFNN, DSH, and DBDH are based on deep learning.
“CNMC [34], IFNN [33], DSH [29] and DBDH [44] are based on deep learning.”
From Deep Perceptual Hash Based on Hash Center for Image Copyright Protection (2022) · cites this paper as [29]
DSH and DBDH are Retrieval Oriented hash schemes.
“DSH and DBDH are Retrieval Oriented hash schemes.”
From Deep Perceptual Hash Based on Hash Center for Image Copyright Protection (2022) · cites this paper as [29]
The curves of DSH, CNMC, and IFNN schemes are very close.
“The curves of DSH [29], CNMC [34] and IFNN [33] schemes are very close.”
From Deep Perceptual Hash Based on Hash Center for Image Copyright Protection (2022) · cites this paper as [29]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · see citing works
Deep hashing for compact binary codes learning
Liong, V. E., Lu, J., Wang, G., Moulin, P., & Zhou, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298862 · Cited by 545 · see citing works
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HashNet: Deep Learning to Hash by Continuation
Cao, Z., Long, M., Wang, J., & Yu, P. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.598 · Cited by 777 · see citing works
Central Similarity Quantization for Efficient Image and Video Retrieval
Yuan, L., Wang, T., Zhang, X., Tay, F. E., Jie, Z., Liu, W., & Feng, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00315 · Cited by 413 · see citing works
Deep Fuzzy Hashing Network for Efficient Image Retrieval
Lu, H., Zhang, M., Xu, X., Li, Y., & Shen, H. T. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2984991 · Cited by 340 · see citing works
Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash
Struppek, L., Hintersdorf, D., Neider, D., & Kersting, K. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533073 · Cited by 153 · see citing works
Fast and scalable search of whole-slide images via self-supervised deep learning
Chen, C., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Schaumberg, A. J., & Mahmood, F. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00929-8 · Cited by 130 · see citing works
Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval
Guo, Y., Ding, G., Liu, L., Han, J., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2652730 · Cited by 108 · see citing works
Deep balanced discrete hashing for image retrieval
Zheng, X., Zhang, Y., & Lu, X. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.04.037 · Cited by 83 · see citing works
Deep Perceptual Hash Based on Hash Center for Image Copyright Protection
Sun, X., & Zhou, J. (2022) · IEEE Access · doi:10.1109/access.2022.3221980 · Cited by 20 · see citing works
- Authors
- Liu, H., Wang, R., Shan, S., & Chen, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 2064-2072
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2464915613
- Cited by
- 933
- References
- 4
- Citations by year
- 2026: 33 · 2025: 65 · 2024: 64 · 2023: 101 · 2022: 91 · 2021: 116 · 2020: 120 · 2019: 155 · 2018: 115 · 2017: 67 · 2016: 4 · 2015: 2
- FWCI
- 39.42
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- binary codes, image retrieval, binary code learning, similarity-preserving hashing, image pairs, quantization, large-scale image databases
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