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Online unsupervised cross-view discrete hashing for large-scale retrieval

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Abstract

Cross-view hashing has shown great potential for large-scale retrieval due to its superiority in terms of computation and storage. In real-world applications, data emerges in a streaming manner, e.g., new images and tags are uploaded to social media by users every day. Existing cross-view hashing methods have to retrain model on new multi-view data, which is time-consuming and not applicable in the real-world applications. To fill this gap, this paper proposes a new online cross-view hashing method, dubbed online unsupervised cross-view discrete hashing (OUCDH) that considers similarity preservation and quantization loss. OUCDH generates hash code as latent embedding shared by multiple views via matrix factorization. OUCDH can well preserve similarity among newly arriving data and old data with the help of anchor graph. An iterative efficient algorithm is developed for online optimization. OUCDH further updates hash code of old data to match that of newly arriving data in each iteration. Extensive experiments on three benchmarks demonstrate that the proposed OUCDH yields superior performance than existing state-of-the-art online cross-view hashing methods.

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References

  1. Cakir F, He K, Bargal SA, Sclaroff S (2017) Mihash: Online hashing with mutual information. In: Proceedings of IEEE International Conference on Computer Vision, pp 437–445

  2. Cakir F, Sclaroff S (2015) Online supervised hashing. In: Proceedings of 2015 IEEE International Conference on Image Processing, pp 2606–2610

  3. Chao G, Luo Y, Ding W (2019) Recent advances in supervised dimension reduction: A survey. Mach Learn Knowl Extract 1(1):341–358

    Article  Google Scholar 

  4. Chao G, Sun S (2016) Alternative multiview maximum entropy discrimination. IEEE Trans Neural Netw Learn Syst 27(7):1445–1456

    Article  MathSciNet  Google Scholar 

  5. Chao G, Sun S (2019) Semi-supervised multi-view maximum entropy discrimination with expectation laplacian regularization. Inf Fusion 45:296–306

    Article  Google Scholar 

  6. Chao G, Sun S, Bi J (2017) A survey on multi-view clustering. arXiv:1712.06246

  7. Chen X, Yang H, Zhao S, King I, Lyu MR (2021) Making online sketching hashing even faster. IEEE Trans Knowl Data Eng 33(3):1089–1101

    Google Scholar 

  8. Chua TS, 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 8th ACM International Conference on Image and Video Retrieval, pp 1–9

  9. Ding G, Guo Y, Zhou J (2014) Collective matrix factorization hashing for multimodal data. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, ppp 2083–2090

  10. Gong Y, Lazebnik S, Gordo A, Perronnin F (2013) 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 

  11. Huiskes MJ, Lew MS (2008) The mir flickr retrieval evaluation. In: Proceedings of the 1st ACM SIGMM International Conference on Multimedia Information Retrieval, pp 39–43

  12. Leng C, Wu J, Cheng J, Bai X, Lu H (2015) Online sketching hashing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 2503–2511

  13. Li C, Zhang H, Chu D, Xu X (2020) SRTM: a supervised relation topic model for multi-classification on large-scale document network. Neural Comput Appl 32(10):6383–6392

    Article  Google Scholar 

  14. Lin M, Ji R, Liu H, Wu Y (2018) Supervised online hashing via hadamard codebook learning. In: Proceedings of 2018 ACM Multimedia Conference on Multimedia Conference. Association for Computing Machinery, pp 1635–1643

  15. Lin Z, Ding G, Mingqing H, Wang J (2015) Semantics-preserving hashing for cross-view retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 3864–3872

  16. Liu L, Lin Z, Shao L, Shen F, Ding G, Han J (2017) Sequential discrete hashing for scalable cross-modality similarity retrieval. IEEE Trans Image Process 26(1):107–118

    Article  MathSciNet  Google Scholar 

  17. Liu W, Mu C, Kumar S, Chang SF (2014) Discrete graph hashing. In: Proceedings of Advances in Neural Information Processing Systems, vol 27. Annual Conference on Neural Information Processing Systems 2014, pp 3419–3427

  18. Lu X, Zhu L, Cheng Z, Li J, Nie X, Zhang H (2019) Flexible online multi-modal hashing for large-scale multimedia retrieval. In: Proceedings of the 27th ACM International Conference on Multimedia. Association for Computing Machinery, pp 1129–1137

  19. Lu X, Zhu L, Li J, Zhang H, Shen HT (2020) Efficient supervised discrete multi-view hashing for large-scale multimedia search. IEEE Trans Multimed 22(8):2048–2060

    Article  Google Scholar 

  20. Peng Y, Huang X, Zhao Y (2018) An overview of cross-media retrieval: concepts, methodologies, benchmarks, and challenges. IEEE Trans Circ Syst Video Technol 28(9):2372–2385

    Article  Google Scholar 

  21. Rasiwasia N, Costa Pereira J, Coviello E, Doyle G, Lanckriet GRG, Levy R, Vasconcelos N (2010) A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th International Conference on Multimedia 2010, pp 251–260

  22. Shen F, Shen C, Liu W, Tao Shen H (2015) Supervised discrete hashing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 37–45

  23. Shen X, Shen F, Liu L, Yuan Y, Liu W, Sun QS (2018) Multiview discrete hashing for scalable multimedia search. ACM Trans Intell Syst Technolo 9(5):53:1–53:21

    Google Scholar 

  24. Shen X, Shen F, Sun QS, Yuan YH (2015) Multi-view latent hashing for efficient multimedia search. In: Proceedings of the 23rd Annual ACM Conference on Multimedia Conference, pp 831–834

  25. Sun S, Xie X, Dong C (2019) Multiview learning with generalized eigenvalue proximal support vector machines. IEEE Trans Cybern 49(2):688–697

    Article  Google Scholar 

  26. Tang J, Wang K, Shao L (2016) Supervised matrix factorization hashing for cross-modal retrieval. IEEE Trans Image Process 25(7):3157–3166

    Article  MathSciNet  Google Scholar 

  27. Wang D, Wang Q, An Y, Gao X, Tian Y (2020) Online collective matrix factorization hashing for large-scale cross-media retrieval. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. Association for Computing Machinery, pp 1409–1418

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

    Article  Google Scholar 

  29. Wang J, Liu W, Kumar S, Chang SF (2016) Learning to hash for indexing big data-a survey. Proc IEEE 104(1):34–57

    Article  Google Scholar 

  30. Wang J, Shen HT, Song J, Ji J (2014) Hashing for similarity search: A survey. arXiv:1408.2927

  31. Weng Z, Zhu Y (2019) Online supervised sketching hashing for large-scale image retrieval. IEEE Access 7:88369–88379

    Article  Google Scholar 

  32. Xie L, Shen J, Zhu L (2016) Online cross-modal hashing for web image retrieval. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, pp 294–300

  33. Xie X, Sun S (2019) General multi-view learning with maximum entropy discrimination. Neurocomputing 332:184–192

    Article  Google Scholar 

  34. Xie X, Sun S (2020) General multi-view semi-supervised least squares support vector machines with multi-manifold regularization. Inf Fusion 62:63–72

    Article  Google Scholar 

  35. Xie X, Sun S (2020) Multi-view support vector machines with the consensus and complementarity information. IEEE Trans Knowl Data Eng 32(12):2401–2413

    Article  Google Scholar 

  36. Xiong Y, Xu Y, Shu X (2020) Cross-view hashing via supervised deep discrete matrix factorization. Pattern Recogn 103:107270

    Article  Google Scholar 

  37. Xu C, Tao D, Xu C (2015) Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell 37(12):2531–2544

    Article  Google Scholar 

  38. Yao T, Wang G, Yan L, Kong X, Su Q, Zhang C, Tian Q (2019) Online latent semantic hashing for cross-media retrieval. Pattern Recogn 89:1–11

    Article  Google Scholar 

  39. Zhang B, Qian J (2021) Autoencoder-based unsupervised clustering and hashing. Appl Intell 51(1):493–505

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 62176126, 61906091, the Natural Science Foundation of Jiangsu Province, China (Youth Fund Project) under Grant No. BK20190440, the Fundamental Research Funds for the Central Universities under Grant No. 30921011210.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing, China

    Xuan Li, Wei Wu & Xiaobo Shen

  2. Department of Computer Science and Technology, Yangzhou University, Yangzhou, China

    Yun-Hao Yuan

  3. Department of Data Science and AI, Faculty of IT, Monash University, Monash, Australia

    Shirui Pan

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  1. Xuan Li
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  2. Wei Wu
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  3. Yun-Hao Yuan
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  4. Shirui Pan
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  5. Xiaobo Shen
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Correspondence to Xiaobo Shen.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

Appendix A: The proof of Theorem 3.1

Appendix A: The proof of Theorem 3.1

Fix Rt and update Bt. The minimizing problem (20) is equivalent to:

$$ \max_{\mathrm{B}_{t}}\text{tr}(\mathrm{B}_{t}{\mathrm{V}_{t}}{~}^{\mathrm{T}}\mathrm{R}_{t}{~}^{\mathrm{T}}) $$
(25)

Due to \(\text {tr}(\mathrm {B}_{t}{\mathrm {V}_{t}}{~}^{\mathrm {T}}\mathrm {R}_{t}{~}^{\mathrm {T}})={\sum }_{i}{\sum }_{j}{\mathrm {B}_{ij}{\hat {\mathrm {V}}}_{ij}}\), where Bij and \({\hat {\mathrm {V}}}_{ij}\) denote the elements of Bt and \(\hat {\mathrm {V}}=\mathrm {R}_{t}\mathrm {V}_{t}\), respectively. We can solve the maximum problem letting Bij = 1 whenever \({\hat {\mathrm {V}}}_{ij}\geq 0\) and − 1 otherwise. That is to say, Bt = sgn(RtVt) in the beginning.

Fix Bt and update Rt.

For fixing Bt, the optimization problem is relevant to a classic Orthogonal Procrustes problem. We compute the SVD of the c × c matrix \(\mathrm {V}_{t}{\mathrm {B}_{t}}^{\mathrm {T}}\) as \({S{\Omega }{\hat {\text {S}}}^{\text {T}}}\) and let \(\mathrm {R}_{t}={\hat {\text {S}}\text {S}^{\text {T}}}\).

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Li, X., Wu, W., Yuan, YH. et al. Online unsupervised cross-view discrete hashing for large-scale retrieval. Appl Intell 52, 14905–14917 (2022). https://doi.org/10.1007/s10489-021-03014-w

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