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Asymmetric similarity-preserving discrete hashing for image retrieval

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Abstract

Hashing methods have been widely studied in the image research community due to their low storage and fast computation. However, generating compact hash codes is still a challenging task. In this paper, we propose a novel Asymmetric Similarity-Preserving Discrete Hashing (ASPDH) method to learn compact binary codes for image retrieval. Specifically, the pairwise similarity matrix is approximated in the asymmetric learning manner with two different real-valued embeddings. In addition, ASPDH constructs two distinct hash functions from the kernel feature and label consistency embeddings. Therefore, similarity preservation and hash code learning can be simultaneously achieved and interactively optimized, which further improves the discriminative capability of the learned binary codes. Then, a well-designed iterative algorithm is developed to efficiently solve the optimization problem, resulting in high-quality binary codes with reduced quantization errors. Extensive experiments on three public datasets show the rationality and effectiveness of our proposed method.

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Data Availability

The data of MIRFlickr that support the findings of this study are openly available at: https://press.liacs.nl/mirflickr/, reference number [44]. The data of MNIST that support the findings of this study are available at: http://yann.lecun.com/exdb/mnist/, reference number [45]. The data of NUS-WIDE that support the findings of this study are available at: https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html, reference number [46].

References

  1. Zhang R, Lin L, Zhang R, Zuo W, Zhang L (2015) Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans Image Process 24(12):4766–4779

    Article  MathSciNet  MATH  Google Scholar 

  2. Zhang B, Qian J, Xie X, Xin Y, Dong Y (2021) Capsnet-based supervised hashing. Appl Intell 51:5912–5926

    Article  Google Scholar 

  3. Zhu L, Tian G, Wang B, Wang W, Zhang D, Li C (2021) Multi-attention based semantic deep hashing for cross-modal retrieval. Appl Intell 51:5927–5939

    Article  Google Scholar 

  4. Hou C, Li Z, Wu J (2021) Unsupervised hash retrieval based on multiple similarity matrices and text self-attention mechanism. Appl Intell 52:7670–7685

    Article  Google Scholar 

  5. Do T-T, Hoang T, Le Tan D-K, Doan A-D, Cheung N-M (2020) Compact hash code learning with binary deep neural network. IEEE Trans Multimedia 22(4):992–1004

    Article  Google Scholar 

  6. Lu X, Zhu L, Cheng Z, Song X, Zhang H (2019) Efficient discrete latent semantic hashing for scalable cross-modal retrieval. Signal Process 154:217–231

    Article  Google Scholar 

  7. Datar M, Immorlica N, Indyk P, Mirrokni VS (2004) Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the annual symposium on computational geometry, pp 253–262

  8. Kulis B, Jain P, Grauman K (2009) Fast similarity search for learned metrics. IEEE Trans Pattern Anal Mach Intell 31(12):2143–2157

    Article  Google Scholar 

  9. Shrivastava A, Li P (2014) Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS). In: Advances in neural information processing systems, pp 2321–2329

  10. Jiang Q-Y, Li W-J (2015) Scalable graph hashing with feature transformation. In: Proceedings of the international joint conference on artificial intelligence, pp 2248–2254

  11. Tu R, Mao X, Wei W (2020) MLS3RDUH: Deep unsupervised hashing via manifold based local semantic similarity structure reconstructing. In: Proceedings of the international joint conference on artificial intelligence, pp 3466–3472

  12. Hansen C, Hansen C, Simonsen JG, Alstrup S, Lioma C (2020) Unsupervised semantic hashing with pairwise reconstruction. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval, pp 2009–2012

  13. Kang W-C, Li W-J, Zhou Z-H (2016) Column sampling based discrete supervised hashing. In: Proceedings of the AAAI conference on artificial intelligence, pp 1230–1236

  14. Liu H, Wang R, Shan S, Chen X (2016) Deep supervised hashing for fast image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2064–2072

  15. Liong VE, Lu J, Duan L-Y, Tan Y (2020) Deep variational and structural hashing. IEEE Trans Pattern Anal Mach Intell 42(3):580–595

    Article  Google Scholar 

  16. Weiss Y, Torralba A, Fergus R (2009) Spectral hashing. In: Advances in neural information processing systems, pp 1753–1760

  17. Gong Y, Lazebnik S, Gordo A, Perronnin F (2012) Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans Pattern Anal Mach Intell 35 (12):2916–2929

    Article  Google Scholar 

  18. Liu W, Wang J, Ji R, Jiang Y-G, Chang S-F (2012) Supervised hashing with kernels. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2074–2081

  19. Shen F, Shen C, Liu W, Tao Shen H (2015) Supervised discrete hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 37–45

  20. Gui J, Liu T, Sun Z, Tao D, Tan T (2018) Fast supervised discrete hashing. IEEE Trans Pattern Anal Mach Intell 40(2):490– 496

    Article  Google Scholar 

  21. Xia R, Pan Y, Lai H, Liu C, Yan S (2014) Supervised hashing for image retrieval via image representation learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 2156– 2162

  22. Shen F, Yang Y, Liu L, Liu W, Tao D, Shen HT (2017) Asymmetric binary coding for image search. IEEE Trans Multimedia 19(9):2022–2032

    Article  Google Scholar 

  23. Zhang Z, Lai Z, Huang Z, Wong WK, Xie G-S, Liu L, Shao L (2019) Scalable supervised asymmetric hashing with semantic and latent factor embedding. IEEE Trans Image Process 28(10):4803–4818

    Article  MathSciNet  MATH  Google Scholar 

  24. Neyshabur B, Srebro N, Salakhutdinov RR, Makarychev Y, Yadollahpour P (2013) The power of asymmetry in binary hashing. In: Advances in neural information processing systems, pp 2823–2831

  25. Luo X, Nie L, He X, Wu Y, Chen Z-D, Xu X-S (2018) Fast scalable supervised hashing. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval, pp 735–744

  26. Li Q, Sun Z, He R, Tan T (2017) Deep supervised discrete hashing. In: Advances in neural information processing systems, pp 2482–2491

  27. Cui Y, Jiang J, Lai Z, Hu Z, Wong W (2018) Supervised discrete discriminant hashing for image retrieval. Pattern Recogn 78:79–90

    Article  Google Scholar 

  28. Lu X, Zheng X, Li X (2016) Latent semantic minimal hashing for image retrieval. IEEE Trans Image Process 26(1):355–368

    Article  MathSciNet  MATH  Google Scholar 

  29. Tang Z, Chen L, Zhang X, Zhang S (2018) Robust image hashing with tensor decomposition. IEEE Trans Knowl Data Eng 31(3):549–560

    Article  Google Scholar 

  30. Ma C, Gong C, Li X, Huang X, Liu W, Yang J (2020) Toward making unsupervised graph hashing discriminative. IEEE Trans Multimedia 22(3):760–774

    Article  Google Scholar 

  31. Dong X, Liu L, Zhu L, Cheng Z, Zhang H (2021) Unsupervised deep k-means hashing for efficient image retrieval and clustering. IEEE Trans Circuits Syst Video Technol 31(8):3266–3277

    Article  Google Scholar 

  32. Lin G, Shen C, Shi Q, Van den Hengel A, Suter D (2014) Fast supervised hashing with decision trees for high-dimensional data. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1963–1970

  33. Liu X, Nie X, Zhou Q, Nie L, Yin Y (2020) Model optimization boosting framework for linear model hash learning. IEEE Trans Image Process 29:4254–4268

    Article  MATH  Google Scholar 

  34. Liu X, Nie X, Dai Q, Huang Y, Lian L, Yin Y (2021) Reinforced short-length hashing. IEEE Trans Circ Syst Video Technol 31(9):3655–3668

    Article  Google Scholar 

  35. Xiang X, Zhang Y, Jin L, Li Z, Tang J (2021) Sub-region localized hashing for fine-grained image retrieval. IEEE Trans Image Process 31:314–326

    Article  Google Scholar 

  36. Yuan L, Wang T, Zhang X, Tay FE, Jie Z, Liu W, Feng J (2020) Central similarity quantization for efficient image and video retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3083–3092

  37. Shen X, Dong G, Zheng Y, Lan L, Tsang IW, Sun Q-S (2022) Deep co-image-label hashing for multi-label image retrieval. IEEE Trans Multimedia 24:1116–1126

    Article  Google Scholar 

  38. Jiang Q-Y, Li W-J (2018) Asymmetric deep supervised hashing. In: Proceedings of the AAAI conference on artificial intelligence, pp 3342–3349

  39. Jiang K, Que Q, Kulis B (2015) Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4933–4941

  40. Chen Y, Tian Z, Zhang H, Wang J, Zhang D (2020) Strongly constrained discrete hashing. IEEE Trans Image Process 29:3596–3611

    Article  MathSciNet  MATH  Google Scholar 

  41. Gordo A, Perronnin F, Gong Y, Lazebnik S (2014) Asymmetric distances for binary embeddings. IEEE Trans Pattern Anal Mach Intell 36(1):33–47

    Article  Google Scholar 

  42. Da C, Xu S, Ding K, Meng G, Xiang S, Pan C (2017) AMVH: asymmetric multi-valued hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 736–744

  43. Wang D, Gao X, Wang X, He L (2018) Label consistent matrix factorization hashing for large-scale cross-modal similarity search. IEEE Trans Pattern Anal Mach Intell 41(10):2466– 2479

    Article  Google Scholar 

  44. Huiskes MJ, Lew MS (2008) The MIR flickr retrieval evaluation. In: Proceedings of the ACM international conference on multimedia information retrieval, pp 39–43

  45. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  46. Chua T-S, Tang J, Hong R, Li H, Luo Z, Zheng Y (2009) NUS-WIDE: a real-world web image database from national university of Singapore. In: Proceedings of the ACM international conference on image and video retrieval, pp 1–9

  47. Wang J, Kumar S, Chang S (2012) Semi-supervised hashing for large-scale search. IEEE Trans Pattern Anal Mach Intell 34(12):2393–2406

    Article  Google Scholar 

  48. Zhang Z, Zhu X, Lu G, Zhang Y (2021) Probability ordinal-preserving semantic hashing for large-scale image retrieval. ACM Trans Knowl Discov Data 15(3):1–22

    Google Scholar 

  49. Long J, Sun L, Guo L, Hua L, Yang Z (2022) Label embedding semantic-guided hashing. Neurocomputing 477:1–13

    Article  Google Scholar 

  50. Cao Z, Long M, Wang J, Yu PS (2017) Hashnet: Deep learning to hash by continuation. In: Proceedings of the IEEE international conference on computer vision, pp 5608– 5617

  51. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  52. Sun Y, Feng S, Ye Y, Li X, Kang J, Huang Z, Luo C (2021) Multi-sensor fusion and explicit semantic preserving-based deep hashing for cross-modal remote sensing image retrieval. IEEE Trans Geosci Remote Sens 60:1–14

    Google Scholar 

  53. Sun Y, Ye Y, Li X, Feng S, Zhang B, Kang J, Dai K (2022) Unsupervised deep hashing through learning soft pseudo label for remote sensing image retrieval. Knowl-Based Syst 239: 107807

    Article  Google Scholar 

  54. Fang J, Fu H, Liu J (2021) Deep triplet hashing network for case-based medical image retrieval. Med Image Anal 69:101981

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the Natural Science Foundation of Shandong Province (No. ZR2020LZH008, ZR2021MF118, ZR2019MF071), the Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (NO.2021CXGC010506, NO.2021SFGC0104), the National Natural Science Foundation of China (No.91846205).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250300, China

    Xiuxiu Ren & Xiangwei Zheng

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250300, China

    Xiuxiu Ren & Xiangwei Zheng

  3. School of Software, Shandong University, Jinan, 250101, China

    Lizhen Cui

  4. Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Gang Wang

  5. School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom

    Huiyu Zhou

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  1. Xiuxiu Ren
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Correspondence to Xiangwei Zheng.

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Ren, X., Zheng, X., Cui, L. et al. Asymmetric similarity-preserving discrete hashing for image retrieval. Appl Intell 53, 12114–12131 (2023). https://doi.org/10.1007/s10489-022-04167-y

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