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Deep Multi-Scale Hashing for Image Retrieval (DMSH)

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Artificial Intelligence: Theories and Applications (ICAITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1769))

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Abstract

In recent years, with the great success of deep learning, deep networks-based hashing has become a leading approach for image retrieval. Most existing deep hashing methods extract the semantic representations only from the last layer, resulting in structure information being ignored which contains additional semantic details that are useful for hash learning. To enhance the image retrieval accuracy by exploring the semantic information and the structure information (local information), We propose a new method of deep hashing called Deep Multi-Scale Hashing (DMSH). This is achieved, firstly, by extracting multiscale features from multiple convolutional layers. Secondly, the features extracted from the convolutional layers are fused to generate more robust representations for efficient image retrieval. The experiments on the CIFAR10 and NUS-WIDE datasets show the superiority of our method.

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References

  1. Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)

    Article  Google Scholar 

  2. Bai, J., et al.: Loopy residual hashing: filling the quantization gap for image retrieval. IEEE Trans. Multimedia 22(1), 215–228 (2019)

    Article  MathSciNet  Google Scholar 

  3. Bai, J., et al.: Deep progressive hashing for image retrieval. IEEE Trans. Multimedia 21(12), 3178–3193 (2019)

    Article  Google Scholar 

  4. Cakir, F., He, K., Bargal, S.A., Sclaroff, S.: Hashing with mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 41(10), 2424–2437 (2019)

    Article  Google Scholar 

  5. Cao, Y., Long, M., Liu, B., Wang, J.: Deep cauchy hashing for hamming space retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1229–1237 (2018)

    Google Scholar 

  6. Cao, Z., Long, M., Wang, J., Yu, P.S.: Hashnet: deep learning to hash by continuation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5608–5617 (2017)

    Google Scholar 

  7. Chen, Z., Zhou, J.: Collaborative multiview hashing. Pattern Recogn. 75, 149–160 (2018)

    Article  Google Scholar 

  8. Chua, T.S., Tang, J., Hong, R., Li, H., Luo, Z., Zheng, Y.: 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 (2009)

    Google Scholar 

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

    Article  Google Scholar 

  10. Erin Liong, V., Lu, J., Wang, G., Moulin, P., Zhou, J.: Deep hashing for compact binary codes learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2475–2483 (2015)

    Google Scholar 

  11. Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. In: Vldb, vol. 99, pp. 518–529 (1999)

    Google Scholar 

  12. Gong, Y., Kumar, S., Rowley, H.A., Lazebnik, S.: Learning binary codes for high-dimensional data using bilinear projections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 484–491 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  14. Gordo, A., Almazán, J., Revaud, J., Larlus, D.: Deep image retrieval: learning global representations for image search. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 241–257. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_15

    Chapter  Google Scholar 

  15. Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the 30th Annual ACM Symposium on Theory of Computing, pp. 604–613 (1998)

    Google Scholar 

  16. Jiang, Q.Y., Li, W.J.: Scalable graph hashing with feature transformation. In: 24th International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  17. Jiang, Q.Y., Li, W.J.: Asymmetric deep supervised hashing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  18. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  19. Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

    Google Scholar 

  20. Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems, vol. 22 (2009)

    Google Scholar 

  21. Lai, H., Pan, Y., Liu, Y., Yan, S.: Simultaneous feature learning and hash coding with deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3270–3278 (2015)

    Google Scholar 

  22. Li, S., Chen, Z., Li, X., Lu, J., Zhou, J.: Unsupervised variational video hashing with 1d-cnn-lstm networks. IEEE Trans. Multimedia 22(6), 1542–1554 (2019)

    Article  Google Scholar 

  23. Lin, G., Shen, C., Shi, Q., Van den Hengel, A., Suter, D.: 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 (2014)

    Google Scholar 

  24. Lin, G., Shen, C., Suter, D., Van Den Hengel, A.: A general two-step approach to learning-based hashing. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2552–2559 (2013)

    Google Scholar 

  25. Lin, G., Shen, C., Wu, J.: Optimizing ranking measures for compact binary code learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 613–627. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_40

    Chapter  Google Scholar 

  26. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2117–2125 (2017)

    Google Scholar 

  27. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  28. Liu, W., Wang, J., Ji, R., Jiang, Y.G., Chang, S.F.: Supervised hashing with kernels. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081. IEEE (2012)

    Google Scholar 

  29. Liu, W., Wang, J., Mu, Y., Kumar, S., Chang, S.F.: Compact hyperplane hashing with bilinear functions. arXiv preprint arXiv:1206.4618 (2012)

  30. Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  31. Shen, F., Gao, X., Liu, L., Yang, Y., Shen, H.T.: Deep asymmetric pairwise hashing. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1522–1530 (2017)

    Google Scholar 

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

    Google Scholar 

  33. Song, J., Gao, L., Liu, L., Zhu, X., Sebe, N.: Quantization-based hashing: a general framework for scalable image and video retrieval. Pattern Recogn. 75, 175–187 (2018)

    Article  Google Scholar 

  34. Strecha, C., Bronstein, A., Bronstein, M., Fua, P.: Ldahash: Improved matching with smaller descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 34(1), 66–78 (2011)

    Article  Google Scholar 

  35. Sun, Y., Yu, S.: Deep supervised hashing with dynamic weighting scheme. In: 2020 5th IEEE International Conference on Big Data Analytics (ICBDA), pp. 57–62. IEEE (2020)

    Google Scholar 

  36. Wang, J., Zhang, T., Sebe, N., Shen, H.T., et al.: A survey on learning to hash. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 769–790 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, vol. 21 (2008)

    Google Scholar 

  39. Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: 28th AAAI Conference on Artificial Intelligence (2014)

    Google Scholar 

  40. Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 16(4), 1–17 (2020)

    Google Scholar 

  41. Yan, C., Shao, B., Zhao, H., Ning, R., Zhang, Y., Xu, F.: 3d room layout estimation from a single RGB image. IEEE Trans. Multimedia 22(11), 3014–3024 (2020)

    Article  Google Scholar 

  42. Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Telecommunications Department, RCAM Laboratory, Sidi Bel Abbès, Algeria

    Adil Redaoui & Kamel Belloulata

  2. High School of Computer Sciences of Sidi Bel Abbes, Sidi Bel Abbès, Algeria

    Amina Belalia

Authors
  1. Adil Redaoui
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  2. Kamel Belloulata
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  3. Amina Belalia
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Corresponding author

Correspondence to Kamel Belloulata .

Editor information

Editors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Mohammed Salem

  2. University of Granada, Granada, Spain

    Juan Julián Merelo

  3. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  4. University of Mascara, Mascara, Algeria

    Rochdi Bachir Bouiadjra

  5. University of Mascara, Mascara, Algeria

    Mohamed Debakla

  6. University of Mascara, Mascara, Algeria

    Fatima Debbat

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Redaoui, A., Belloulata, K., Belalia, A. (2023). Deep Multi-Scale Hashing for Image Retrieval (DMSH). In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-28540-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28539-4

  • Online ISBN: 978-3-031-28540-0

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