Deep hashing for compact binary codes learning
Abstract
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search. Unlike most existing binary codes learning methods which seek a single linear projection to map each sample into a binary vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the nonlinear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the deep network: 1) the loss between the original real-valued feature descriptor and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) by including one discriminative term into the objective function of DH which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes. Experimental results show the superiority of the proposed approach over the state-of-the-arts.
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One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning ObjectivePreprint
Hoe, J. T., Ng, K. W., Zhang, T., Chan, C. S., Song, Y.-Z., & Xiang, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14449 · Cited by 98 · see citing works
Supervised Discrete Hashing With Relaxation
Gui, J., Liu, T., Sun, Z., Tao, D., & Tan, T. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2636870 · Cited by 95 · see citing works
Generative Domain-Migration Hashing for Sketch-to-Image Retrieval
Zhang, J., Shen, F., Liu, L., Zhu, F., Yu, M., Shao, L., Shen, H. T., & Gool, L. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_19 · Cited by 95 · see citing works
Fast Scalable Supervised Hashing
Luo, X., Nie, L., He, X., Wu, Y. L., Chen, Z.-D., & Xu, X.-S. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210035 · Cited by 93 · see citing works
Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval
Zhang, H., Liu, L., Long, Y., & Shao, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781422 · Cited by 92 · see citing works
Zhang, H., Wang, M., Hong, R., & Chua, T. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2964308 · Cited by 91 · see citing works
Supervised deep hashing for scalable face image retrieval
Tang, J., Li, Z., & Zhu, X. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.03.028 · Cited by 91 · see citing works
Deep Semantic Hashing with Generative Adversarial Networks
Qiu, Z., Pan, Y., Yao, T., & Mei, T. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3080842 · Cited by 91 · see citing works
Binary Hashing for Approximate Nearest Neighbor Search on Big Data: A Survey
Cao, Y., Qi, H., Zhou, W., Kato, J., Li, K., Liu, X., & Gui, J. (2017) · IEEE Access · doi:10.1109/access.2017.2781360 · Cited by 90 · see citing works
Sitcom-star-based clothing retrieval for video advertising: a deep learning framework
Zhang, H., Ji, Y., Huang, W., & Liu, L. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3579-x · Cited by 87 · see citing works
Adaptive hash retrieval with kernel based similarity
Bai, X. P., Yan, C., Yang, H., Bai, L., Zhou, J. W., & Hancock, E. R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.03.020 · Cited by 87 · see citing works
Self-citationDeep 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
Hierarchical Clustering-Based Graphs for Large Scale Approximate Nearest Neighbor Search
Muñoz, J. A. V., Gonçalves, M. A., Dias, Z., & Torres, R. D. S. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.106970 · Cited by 84 · see citing works
Two-Stream Deep Hashing With Class-Specific Centers for Supervised Image Search
Deng, C. D., Yang, E., Liu, T., & Tao, D. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929068 · Cited by 84 · see citing works
Learning binary codes with neural collaborative filtering for efficient recommendation systems
Li, Y., Wang, S., Pan, Q., Peng, H., Yang, T., & Cambria, E. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.012 · Cited by 81 · see citing works
Learning Deep Binary Descriptor with Multi-quantization
Duan, Y., Lu, J., Wang, Z., Feng, J., & Zhou, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.516 · Cited by 80 · see citing works
Self-citationWeakly Supervised Multimodal Hashing for Scalable Social Image Retrieval
Tang, J., & Li, Z. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2715227 · Cited by 79 · see citing works
Deep Semantic-Preserving Ordinal Hashing for Cross-Modal Similarity Search
Jin, L., Li, K., Li, Z., Xiao, F., Qi, G.-J., & Tang, J. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2869601 · Cited by 77 · see citing works
Deep Hashing Network for Unsupervised Domain Adaptation
Ramakrishnan, R., Nagabandi, B., Eusébio, J., Chakraborty, S., Venkateswara, H., & Panchanathan, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-45529-3_4 · Cited by 101 · see citing works
Distributed Adaptive Binary Quantization for Fast Nearest Neighbor Search
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TransHash: Transformer-based Hamming Hashing for Efficient Image Retrieval
Chen, Y., Zhang, S., Liu, F., Chang, Z., Ye, M., & Qi, Z. (2022) · International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531405 · Cited by 76 · see citing works
Variational Deep Semantic Hashing for Text Documents
Chaidaroon, S., & Fang, Y. (2017) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3077136.3080816 · Cited by 75 · see citing works
Unsupervised Deep Learning of Compact Binary Descriptors
Lin, K., Lu, J., Chen, C., Zhou, J., & Sun, M. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2833865 · Cited by 75 · see citing works
Self-citationSupervised Quantization for Similarity Search
Wang, X., Zhang, T., Qi, G.-J., Tang, J., & Wang, J. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.222 · Cited by 74 · see citing works
Discriminative Deep Quantization Hashing for Face Image Retrieval
Tang, J., Lin, J., Li, Z., & Yang, J. J. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2816743 · Cited by 72 · see citing works
Deep Secure Encoding for Face Template Protection
Pandey, R., Zhou, Y., Kota, B. U., & Govindaraju, V. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.17 · Cited by 71 · see citing works
Learning binary code for fast nearest subspace search
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Self-citationUnified Binary Generative Adversarial Network for Image Retrieval and Compression
Song, J., He, T., Gao, L., Xu, X., Hanjalić, A., & Shen, H. T. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01305-2 · Cited by 67 · see citing works
Deep face fuzzy vault: Implementation and performance
Rathgeb, C., Merkle, J., Scholz, J., Tams, B., & Nesterowicz, V. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102539 · Cited by 67 · see citing works
Structured deep hashing with convolutional neural networks for fast person re-identification
Wu, L. Y., Wang, Y., Ge, Z., Hu, Q., & Li, X. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.11.009 · Cited by 66 · see citing works
Inductive Structure Consistent Hashing via Flexible Semantic Calibration
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ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval
Cui, Q., Jiang, Q.-Y., Wei, X.-S., Li, W.-J., & Yoshie, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58580-8_12 · Cited by 66 · see citing works
Unsupervised Binary Representation Learning with Deep Variational Networks
Shen, Y., Liu, L., & Shao, L. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01166-4 · Cited by 66 · see citing works
Deep triplet hashing network for case-based medical image retrieval
Fang, J., Fu, H., & Liu, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101981 · Cited by 66 · see citing works
- Authors
- Liong, V. E., Lu, J., Wang, G., Moulin, P., & Zhou, J.
- Affiliations
- Advanced Digital Sciences Center (SG)
- Nanyang Technological University (SG)
- University of Illinois Urbana-Champaign (US)
- Tsinghua University (CN)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 2475-2483
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1956333070
- Cited by
- 545
- References
- 40
- Citations by year
- 2026: 2 · 2025: 17 · 2024: 19 · 2023: 28 · 2022: 46 · 2021: 49 · 2020: 70 · 2019: 82 · 2018: 88 · 2017: 102 · 2016: 33 · 2015: 9
- FWCI
- 31.71
- 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
- deep hashing, binary code learning, compact binary codes, visual search, large-scale image retrieval
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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