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Triplet-object loss for large scale deep image retrieval

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Abstract

Deep hashing has been widely applied in large scale image retrieval due to its high computation efficiency and retrieval performance. Recently, training deep hashing networks with a triplet ranking loss become a common framework. However, most of the triplet ranking loss based deep hashing methods cannot obtain satisfactory retrieval performance due to their ignoring the relative similarities among the objects. In this paper, we propose a method to learn the discriminative object features and utilize these features to compute the adaptive margins of the proposed loss for learning powerful hash codes. Experimental results show that our learned hash codes can yield state-of-the-art retrieval performance on three challenging datasets

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References

  1. Habimana O, Li Y, Li R et al (2020) Attentive convolutional gated recurrent network: a contextual model to sentiment analysis. Int J Mach Learn Cybern 11:2637–2651

    Article  Google Scholar 

  2. Zhu J, Wu S, Zhu H et al (2019) Multi-center convolutional descriptor aggregation for image retrieval. Int J Mach Learn Cybern 10(7):1863–1873

    Article  Google Scholar 

  3. Yao T, Long F, Mei T, et al (2016) Deep semantic-preserving and ranking-based hashing for image retrieval. In: International joint conference on artificial intelligence, pp 3931–3937

  4. Lai H, Pan Y, Liu Y, et al (2015) Simultaneous feature learning and hash coding with deep neural networks. In: Computer vision and pattern recognition, pp 3270–3278

  5. Jin X, Sun W, Jin Z (2020) A discriminative deep association learning for facial expression recognition. Int J Mach Learn Cybern 11(4):779–793

    Article  Google Scholar 

  6. Kalantidis Y, Mellina C, Osindero S (2016) Cross-dimensional weighting for aggregated deep convolutional features. In: European conference on computer vision, pp 685–701

  7. Xu J, Shi C, Chengzuo Q, et al (2018) Unsupervised part-based weighting aggregation of deep convolutional features for image retrieval. In: AAAI Conference on artificial intelligence, pp 7436–7443

  8. Shen F, Shen C, Liu W, et al (2015) Supervised discrete hashing. In: Computer vision and pattern recognition, pp 37–45

  9. Ng WWY, Jiang X, Tian X et al (2020) Incremental hashing with sample selection using dominant sets. Int J Mach Learn Cybern 11(12):2689–2702

    Article  Google Scholar 

  10. Li S, Chen Z, Lu J, et al (2019) Neighborhood preserving hashing for scalable video retrieval. In: International conference on computer vision, pp: 8212–8221.

  11. Li J, Ng WW, Tian X et al (2020) Weighted multi-deep ranking supervised hashing for efficient image retrieval. Int J Mach Learn Cybern 11(4):883–897

    Article  Google Scholar 

  12. Zhang J, Peng Y (2018) Query-adaptive image retrieval by deep-weighted hashing. IEEE T Multim 20(9):2400–2414

    Article  Google Scholar 

  13. Deng C, Yang E, Liu T et al (2019) Unsupervised semantic-preserving adversarial hashing for image search. IEEE T Image Process 28(8):4032–4044

    Article  MathSciNet  Google Scholar 

  14. Do T, Doan A, Cheung N (2016) Learning to hash with binary deep neural network. In: European conference on computer vision, pp 219–234

  15. Shen F, Xu Y, Liu L et al (2018) Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE T Pattern Anal 40(12):3034–3044

    Article  Google Scholar 

  16. Shen Y, Liu L, Shao L (2019) Unsupervised binary representation learning with deep variational networks. Int J Comput Vision 127:1614–1628

    Article  Google Scholar 

  17. Tian Z, Zhang H, Chen Y, et al (2020) Unsupervised hashing based on the recovery of subspace structures. Pattern Recogn. https://doi.org/10.1016/j.patcog.2020.107261

  18. Xia R, Pan Y, Lai H, et al (2014) Supervised hashing for image retrieval via image representation learning. In: National conference on artificial intelligence, pp 2156–2162

  19. Wang D, Gao X, Wang X et al (2016) Multimodal discriminative binary embedding for large-scale cross-modal retrieval. IEEE T Image Process 25(10):4540–4554

    Article  MathSciNet  Google Scholar 

  20. Xu X, Shen F, Yang Y et al (2017) Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE T Image Process 26(5):2494–2507

    Article  MathSciNet  Google Scholar 

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

  22. Zhu H, Long M, Wang J, et al (2016) Deep hashing network for efficient similarity retrieval. In: National conference on artificial intelligence, pp 2415–2421

  23. Liu H, Wang R, Shan S, Chen X (2016) Deep Supervised Hashing for Fast Image Retrieval. In: IEEE conference on computer vision and pattern recognition, pp. 2064–2072.

  24. Junjie C, Cheung WK, Wang A (2018) Learning deep unsupervised binary codes for image retrieval. In: International joint conference on artificial intelligence, pp 613–619

  25. Huang C, Loy CC, Tang X (2016) Unsupervised learning of discriminative attributes and visual representations. Computer vision and pattern recognition, pp 5175–5184

  26. Deng C, Chen Z, Liu X et al (2018) Triplet-based deep hashing network for cross-modal retrieval. IEEE T Image Process 27(8):3893–3903

    Article  MathSciNet  Google Scholar 

  27. Dai P, Ji R, Wang H, et al (2018) Cross-modality person re-identification with generative adversarial training. In: International joint conference on artificial intelligence, pp 677–683

  28. Min W, Mei S, Li Z et al (2020) A Two-Stage triplet network training framework for image retrieval. IEEE T Multim. https://doi.org/10.1109/TMM.2020.2974326

    Article  Google Scholar 

  29. Norouzi M, Fleet D J, Salakhutdinov R (2012) Hamming distance metric learning. In: Neural information processing systems, pp 1061–1069

  30. Chen W, Chen X, Zhang J, et al (2017) Beyond triplet loss: a deep quadruplet network for person re-identification. In: Computer vision and pattern recognition, pp 1320–1329

  31. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arxiv: 1409.1556

  32. Tolias G, Sicre R, Jegou H (2016) Particular object retrieval with integral max-pooling of CNN activations. International conference on learning representations, pp 1–12

  33. Wei X, Luo J, Wu J et al (2017) Selective convolutional descriptor aggregation for fine-grained image retrieval. IEEE T Image Process 26(6):2868–2881

    Article  MathSciNet  Google Scholar 

  34. Fu Z, Robleskelly A, Zhou J (2011) Milis: multiple instance learning with instance selection. IEEE T Pattern Anal 33(5):958–977

    Article  Google Scholar 

  35. Krizhevsky A (2009) Learning multiple layers of features from tiny images, Technical Report, Computer Science Department, University of Toronto

  36. Chua T, Tang J, Hong R, Li H (2009) Nus-wide: a real-world web image database from national university of Singapore. In: ACM conference on image and video retrieval, pp 1–9

  37. Netzer Y, Wang T, Coates A (2012) Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning, pp 1–9

  38. Gong Y, Lazebnik S, Gordo A et al (2013) Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE T Pattern Anal 35(12):2916–2929

    Article  Google Scholar 

  39. Zha Y, Qiu Z, Zhang P et al (2020) Unsupervised ensemble hashing: boosting minimum hamming distance. IEEE Access: 42937–42947

  40. Liu W, Wang J, Ji R, et al (2012) Supervised hashing with kernels. In: Computer vision and pattern recognition, pp 2074–2081

  41. Yu T, Yuan J, Fang C, et al (2018) Product quantization network for fast image retrieval. In: European conference on computer vision, pp 191–206

  42. Cao Y, Long M, Liu B et al (2018) Deep cauchy hashing for hamming space retrieval. In: Computer vision and pattern recognition, pp 1229–1237

  43. Yang J, Zhang Y, Feng R et al (2020) Deep reinforcement hashing with redundancy elimination for effective image retrieval. Pattern Recogn. https://doi.org/10.1016/j.patcog.2019.107116

    Article  Google Scholar 

  44. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: International conference on machine learning, pp 148–156

  45. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Neural information processing systems, pp 1097–1105

  46. Lin M, Chen Q, Yan S (2013) Network in network. arXiv:1312.4400

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Acknowledgements

This work is supported by the Youth Fund Project of Hebei Natural Science Foundation (F2018511002), the National Natural Science Foundation of China (Grant 61802269), the National Social Science Foundation of China (17BTQ068).

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Authors and Affiliations

  1. Department of Information Management, The National Police University for Criminal Justice, Shenyang, China

    Jie Zhu & Yang Shu

  2. College of Computer Science and Technology, China University of Petroleum, Beijing, China

    Junsan Zhang

  3. College of Science and Engineering, University of Glasgow, Glasgow, UK

    Xuanye Wang

  4. College of Management, Hebei University, Baoding, China

    Shufang Wu

Authors
  1. Jie Zhu
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  2. Yang Shu
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  3. Junsan Zhang
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  4. Xuanye Wang
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  5. Shufang Wu
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Correspondence to Shufang Wu.

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Zhu, J., Shu, Y., Zhang, J. et al. Triplet-object loss for large scale deep image retrieval. Int. J. Mach. Learn. & Cyber. 13, 1–9 (2022). https://doi.org/10.1007/s13042-021-01330-8

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