The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Survey on Learning to Hash
Open accessAbstract
Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.
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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
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Approximate nearest neighbors: towards removing the curse of dimensionality
Indyk, P., & Motwani, R. (1998) · Proceedings of the thirtieth annual ACM symposium on Theory of computing - STOC '98 · doi:10.1145/276698.276876 · Cited by 4,188
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 · cited 8×
“BRE = binary reconstructive embedding [66], MLH = minimal loss hashing [107], LSH = locality sensitive hashing [14], ITQ = iterative quantization [35], [36], SH = spectral hashing [156], AGH-2 = two-layer hashing with graphs [86], USPLH = unsupervised sequential projection learning hashing [143], PQ = product quantiza…”
BackgroundMethodResultInfluentialHamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901 · cited 1×
“The reference set is from the Holiday images combined with Flickr1M [49].”
MethodNear-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Andoni, A., & Indyk, P. (2006) · 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) · doi:10.1109/focs.2006.49 · Cited by 1,067
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
High-dimensional signature compression for large-scale image classification
Sanchez, J., & Perronnin, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995504 · Cited by 272
Cited by
Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478
A Survey on Deep Hashing Methods
Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., & Hua, X.-S. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532624 · Cited by 241
Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing
Ding, G., Guo, Y., Zhou, J., & Gao, Y. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2607421 · Cited by 218
Dense Text Retrieval Based on Pretrained Language Models: A Survey
Zhao, W. X., Liu, J., Ren, R., & Wen, J.-R. (2024) · ACM Transactions on Information Systems · doi:10.1145/3637870 · Cited by 211
“• Hashing-based approaches [248], [254], [255] assign vec-”
Controllable Video Captioning With POS Sequence Guidance Based on Gated Fusion Network
Wang, B., Ma, L., Zhang, W., Jiang, W., Wang, J., & Liu, W. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00273 · Cited by 199
Self-citation
Metadata
- Authors
- Wang, J., Zhang, T., Song, J., Sebe, N., & Shen, H. T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2018-04-01
- Volume
- 40
- Issue
- 4
- Pages
- 769-790
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2411707397
- Cited by
- 971
- References
- 10
- FWCI
- 46.72
- Type
- journal article
- Language
- en
- Open access
- green
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