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Weighted multi-deep ranking supervised hashing for efficient image retrieval

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Abstract

Deep hashing has proven to be efficient and effective for large-scale image retrieval due to the strong representation capability of deep networks. Existing deep hashing methods only utilize a single deep hash table. In order to achieve both higher retrieval recall and precision, longer hash codes can be used but at the expense of higher space usage. To address this issue, a novel deep hashing method is proposed in this paper, weighted multi-deep ranking supervised hashing (WMDRH), which employs multiple weighted deep hash tables to improve precision/recall without increasing space usage. The hash table is constructed as an additional layer in a deep network. Hash codes are generated by minimizing the loss function that contains two terms: (1) the ranking pairwise loss and (2) the classification loss. The ranking pairwise loss ensures to generate discriminative hash codes by penalizing more for the (dis)similar image pairs with (small)large Hamming distances. The classification loss guarantees the hash codes to be effective for category prediction. Different hash bits in each individual hash table are treated differently by assigning corresponding weights based on information preservation and bit diversity. Moreover, multiple hash tables are integrated by assigning the appropriate weight to each table according to its mean average precision (MAP) score for image retrieval. Experiments on three widely-used image databases show the proposed method outperforms state-of-the-art hashing methods.

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References

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

  2. 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 

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

  4. Norouzi M,Fleet DJ (2011) Minimal loss hashing for compact binary codes. In: Proc. international conference on machine learning, pp 353–360

  5. Dniz G, Bueno J, la Salido TFD (2011) Face recognition using histograms of oriented gradients. Proc Pattern Recogn Lett 32(12):1598–1603

    Article  Google Scholar 

  6. Huang D, Shan C, Ardabilian M, Wang Y, Chen L (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst Man Cybern 41(6):765–781

    Article  Google Scholar 

  7. Ahonen T, Rahtu E, Ojansivu V, Heikkila J (2008) Recognition of blurred faces using local phase quantization. In: Proc. international conference on pattern recognition

  8. Paul E, Ajeena BAS (2015) Mining images for image annotation using surf detection technique. In: Proc. international conference on control, communication and computing India

  9. Purandare V, Talele KT (2014) Efficient heterogeneous face recognition using scale invariant feature transform. In: Proc. international conference on circuits, systems, communication and information technology applications

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

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

  12. Lai H, Pan Y, Liu Y, Yan S (2015) Simultaneous feature learning and hash coding with deep neural networks, pp 3270–3278

  13. Yao T, Long F, Mei T, Rui Y (2016) Deep semantic-preserving and ranking-based hashing for image retrieval. In: Proc. international joint conference on artificial intelligence, pp 3931–3937

  14. 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  Google Scholar 

  15. Lin J, Li Z, Tang J (2017) Discriminative deep hashing for scalable face image retrieval. In: Proc. international joint conference on artificial intelligence, pp 2266–2272

  16. Xu H, Wang J, Li Z, Zeng G, Li S, Yu N (2011) Complementaryhashing for approximate nearest neighbor search. In: Proc international conference on computer vision

  17. Li P, Cheng J, Lu H (2013) Hashing with dual complementary projection learning for fast image retrieval. Proc Neurocomput 120(10):83–89

    Article  Google Scholar 

  18. Raginsky M, Lazebnik S (2009) Locality-sensitive binary codes from shift-invariant kernels. In: Proc of Conf and workshop on neural information processing systems, pp 1509–1517

  19. Ng WWY, Lv Y, Zeng Z, Yeung DS, Chan PPK (2017) Sequential conditional entropy maximization semi-supervised hashing for semantic image retrieval. Int J Mach Learn Cybern 8(2):571–586

    Article  Google Scholar 

  20. Liu Y, Feng L, Liu S, Sun M (2019) An ELM based local topology preserving hashing. Int J Mach Learn Cybern 2019:1–18

    Google Scholar 

  21. Kulis B, Grauman K (2009) Kernelized locality-sensitive hashing for scalable image search. In: Proc of IEEE Int Conf on computer vision, pp 2130–2137

  22. Lv Y, Ng WW, Zeng Z, Yeung DS, Chan PP (2015) Asymmetric cyclical hashing for large scale image retrieval. IEEE Trans Multimedia 17(8):1225–1235

    Article  Google Scholar 

  23. Guo Y, Ding G, Liu L, Han J, Shao L (2017) Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Trans Image Process 26(3):1344–1354

    Article  MathSciNet  Google Scholar 

  24. Liu X, Du B, Deng C, Liu M, Lang B (2016) Structure sensitive hashing with adaptive product quantization. IEEE Trans Cybern 46(10):2252–2264

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Wu C, Zhu J, Cai D, Chen C, Bu J (2013) Semi-supervised nonlinear hashing using bootstrap sequential projection learning. IEEE Trans Knowl Data Eng 25(6):1380–1393

    Article  Google Scholar 

  27. Liu W, Wang J, Ji R, Jiang Y-G, Chang S-F (2012) Supervised hashing with kernels. In: Proc. computer vision and pattern recognition, pp 2074–2081

  28. Kulis B, Darrell T (2009) Learning to hash with binary reconstructive embeddings. In: Proc. neural information processing systems, pp 1042–1050

  29. Song D, Liu RJW, Meyer DA, Smith JR (2015) Top rank supervised binary coding for visual search. In: Proc. IEEE international conference on computer vision, pp 1922–1930

  30. Ng WWY, Li J, Feng S, Yeung DS, Chan PPK (2015) Sensitivity based image filtering for multi-hashing in large scale image retrieval problems. Int J Mach Learn Cybern 6(5):777–794

    Article  Google Scholar 

  31. Salakhutdinov R, Hinton G (2009) Semantic hashing. Proc Int J Approx Reasoning 50(7):969–978

    Article  Google Scholar 

  32. Zhu H, Long M, Wang J, Cao Y (2016) Deep hashing network for efficient similarity retrieval. In: Proc AAAI conference on artificial intelligence, pp 2415–2421

  33. Li W, Wang S, Kang W (2016) Feature learning based deep supervised hashing with pairwise labels. In: Proc. international joint conference on artificial intelligence, pp 1711–1717

  34. Wang X, Shi Y, Kitani K (2016) Deep supervised hashing with triplet labels. In: Proc. Asian conference on computer vision, pp 70–84

    Chapter  Google Scholar 

  35. Liu Y, Song J, Zhou K, Yan L, Liu L, Zou F, Shao L (2018) Deep self-taught hashing for image retrieval. IEEE Trans Cybern 2018:1–13

    Article  Google Scholar 

  36. Galton F (1892) Finger prints. Macmillan, London

    Google Scholar 

  37. 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: Proceeding crence on image and video retrieval. ACM, p 48

  38. Liu W, Wang J, Ji R, Jiang Y, Chang S (2012) Supervised hashing with kernels. In: Proc. IEEE conference on computer vision and pattern recognition

  39. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proc. advances in neural information processing systems

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Acknowledgements

This work was supported by National Natural Science Foundation of China under Grants 61876066, 61572201 and 61672443, Guangzhou Science and Technology Plan Project 201804010245, Hong Kong RGC General Research Funds under Grant 9042038 (CityU 11205314) and Grant 9042322 (CityU 11200116), and EU Horizon 2020 Programme (700381, ASGARD).

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Authors and Affiliations

  1. Guangdong Provincial Key Lab of Computational Intelligence & Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Jiayong Li, Wing W. Y. Ng & Xing Tian

  2. Department of Computer Science, Hong Kong City University, Kowloon Tong, Hong Kong

    Sam Kwong

  3. School of Computing, Ulster University, Jordanstown, UK

    Hui Wang

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  1. Jiayong Li
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  2. Wing W. Y. Ng
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  3. Xing Tian
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  4. Sam Kwong
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  5. Hui Wang
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Correspondence to Wing W. Y. Ng or Xing Tian.

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Li, J., Ng, W.W.Y., Tian, X. et al. Weighted multi-deep ranking supervised hashing for efficient image retrieval. Int. J. Mach. Learn. & Cyber. 11, 883–897 (2020). https://doi.org/10.1007/s13042-019-01026-0

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  • DOI: https://doi.org/10.1007/s13042-019-01026-0

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