Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

DSHPoolF: deep supervised hashing based on selective pool feature map for image retrieval

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Deep supervised hashing has turned up to unravel many large-scale image retrieval challenges. Although deep supervised hashing accomplishes good results for image retrieval process, requisite for further improving the retrieval accuracy always remains the primal focus of interest. In Deep hashing methods, feature representation happens at the outset of the fully connected (FC) layers, causing shortage of spatial information owing to its global nature, whereas deeper pooling layers preserve semantically similar information by retaining the images spatial information, which can result in uplifting the retrieval performance. Hereby, for enhancing the image retrieval accuracy through exploring spatial information, a novel way of deep supervised hashing based on Pooled Feature map (DSHPoolF) is proposed to generate compact hash codes that explore the spatial information by weighing the informative Feature maps from the last pooling layer. This is achieved, firstly, by weighing the last pooling layers Feature map in two ways, namely average–max-based pooling and probability-based pooling strategies. Secondly, informative Feature maps are selected with the help of the weights. In addition to this, the informative Feature maps play a key role in optimizing quantization error together with the loss function and classification errors in a single-step, point-wise ranking manner. This proposed DSHPoolF method is assessed using three datasets (CIFAR-10, MNIST and ImageNet) that unveils primitive outcome in comparison with other existing prominent hash-based methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Agrawal, A., Mittal, N.: Using cnn for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy. Vis. Comput. 36(2), 405–412 (2020)

    Article  Google Scholar 

  2. Ahmed, K.T., Ummesafi, S., Iqbal, A.: Content based image retrieval using image features information fusion. Inf. Fusion 51, 76–99 (2019)

    Article  Google Scholar 

  3. Alzu’bi, A., Amira, A., Ramzan, N.: Content-based image retrieval with compact deep convolutional features. Neurocomputing 249, 95–105 (2017)

    Article  Google Scholar 

  4. Alzubi, A., Amira, A., Ramzan, N.: Semantic content-based image retrieval: a comprehensive study. J. Vis. Commun. Image Represent. 32, 20–54 (2015)

    Article  Google Scholar 

  5. Arulmozhi, P., Abirami, S.: A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval. Artif. Intell. Rev. 52(1), 323–355 (2019)

    Article  Google Scholar 

  6. Bianco, S., Celona, L., Napoletano, P., Schettini, R.: On the use of deep learning for blind image quality assessment. Signal Image Video Process. 12(2), 355–362 (2018)

    Article  Google Scholar 

  7. 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)

  8. Celik, C., Bilge, H.S.: Content based image retrieval with sparse representations and local feature descriptors: a comparative study. Pattern Recognit. 68, 1–13 (2017)

    Article  Google Scholar 

  9. Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 1109–1135 (2010)

    MathSciNet  MATH  Google Scholar 

  10. Cheng, J.D., Sun, Q.L., Zhang, J.X., Desrosiers, C., Liu, B., Lu, J., Zhang, Q.: Deep high-order supervised hashing. Optik 180, 847–857 (2019)

    Article  Google Scholar 

  11. Cheng, S., Lai, H., Wang, L., Qin, J.: A novel deep hashing method for fast image retrieval. Vis. Comput. 35(9), 1255–1266 (2019)

    Article  Google Scholar 

  12. Datar, M. , Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262 (2004)

  13. 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)

  14. Esmaeili, M.M., Ward, R.K., Fatourechi, M.: A fast approximate nearest neighbor search algorithm in the hamming space. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2481–2488 (2012)

    Article  Google Scholar 

  15. 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 

  16. Gonzalez-Garcia, A., Modolo, D., Ferrari, V.: Do semantic parts emerge in convolutional neural networks? Int. J. Comput. Vis. 126(5), 476–494 (2018)

    Article  MathSciNet  Google Scholar 

  17. Gordo, A., Almazán, J., Revaud, J., Larlus, D.: Deep image retrieval: learning global representations for image search. In: European Conference on Computer Vision, pp. 241–257. Springer (2016)

  18. Gordo, A., Almazan, J., Revaud, J., Larlus, D.: End-to-end learning of deep visual representations for image retrieval. Int. J. Comput. Vis. 124(2), 237–254 (2017)

    Article  MathSciNet  Google Scholar 

  19. Gorisse, D., Cord, M., Precioso, F.: Locality-sensitive hashing for chi2 distance. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 402–409 (2011)

    Article  Google Scholar 

  20. Grauman, K., Fergus, R.: Learning binary hash codes for large-scale image search. In: Machine Learning for Computer Vision, pp. 49–87. Springer (2013)

  21. Guan, H., Cheng, B.: How do deep convolutional features affect tracking performance: an experimental study. Vis. Comput. 34(12), 1701–1711 (2018)

    Article  Google Scholar 

  22. He, T., Li, X.: Image quality recognition technology based on deep learning. J. Vis. Commun. Image Represent. 65, 102654 (2019)

    Article  Google Scholar 

  23. Islam, S.M., Banerjee, M., Bhattacharyya, S.: Chakraborty, Susanta:Content-based image retrieval based on multiple extended fuzzy-rough framework. Appl. Soft Comput. 57, 102–117 (2017)

    Article  Google Scholar 

  24. Ji, J., Li, J., Yan, S., Zhang, B., Tian, Q.: Super-bit locality-sensitive hashing. In: Advances in Neural Information Processing Systems, pp. 108–116 (2012)

  25. Jiang, Q.-Y., Cui, X., Li, W.-J.: Deep discrete supervised hashing. IEEE Trans. Image Process. 27(12), 5996–6009 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  26. Kan, S., Cen, L., Zheng, X., Cen, Y., Zhu, Z., Wang, H.: A supervised learning to index model for approximate nearest neighbor image retrieval. Signal Process. Image Commun. 78, 494–502 (2019)

    Article  Google Scholar 

  27. Komorowski, M., Trzciński, T.: Random binary search trees for approximate nearest neighbour search in binary spaces. Appl. Soft Comput. 79, 87–93 (2019)

    Article  Google Scholar 

  28. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

  29. Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Advances in Neural Information Processing Systems, pp. 1042–1050 (2009)

  30. Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. ICCV 9, 2130–2137 (2009)

    Google Scholar 

  31. 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)

  32. LeCun, Y.: The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/ (1998)

  33. Li, J., Ng, W.W.Y., Tian, X., Kwong, S., Wang, H.: Weighted multi-deep ranking supervised hashing for efficient image retrieval. Int. J. Mach. Learn. Cybern. 2019, 1–15 (2019)

    Google Scholar 

  34. Li, Q., Sun, Z., He, R., Tan, T.: A general framework for deep supervised discrete hashing. Int. J. Comput. Vis. 2020, 1–19 (2020)

    MathSciNet  Google Scholar 

  35. Li, W.-J., Wang, S., Kang, W.-C.: Feature learning based deep supervised hashing with pairwise labels. arXiv preprintarXiv:1511.03855 (2015)

  36. Li, Z., Tang, J., Zhang, L., Yang, J.: Weakly-supervised semantic guided hashing for social image retrieval. Int. J. Comput. Vis. (2020)

  37. Lin, J., Li, Z., Tang, J.: Discriminative deep hashing for scalable face image retrieval. In: IJCAI, pp. 2266–2272 (2017)

  38. Lin, K., Yang, H.-F., Hsiao, J.-H., Chen, C.-S.: Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 27–35 (2015)

  39. Liu, H., Wang, R., Shan, S., Chen, X.: Deep supervised hashing for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2064–2072 (2016)

  40. Liu, H., Ji, R., Wang, J., Shen, C.: Ordinal constraint binary coding for approximate nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 941–955 (2018)

    Article  Google Scholar 

  41. 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)

  42. Liu, W., Ma, H., Qi, H., Zhao, D., Chen, Z.: Deep learning hashing for mobile visual search. EURASIP J. Image Video Process. 2017(1), 1–11 (2017)

    Google Scholar 

  43. Ma, Q., Bai, C., Zhang, J., Liu, Z., Chen, S.: Supervised learning based discrete hashing for image retrieval. Pattern Recognit. 92, 156–164 (2019)

    Article  Google Scholar 

  44. Ma, W., Yuanwei, W., Cen, F., Wang, G.: Mdfn: multi-scale deep feature learning network for object detection. Pattern Recognit. 100, 107149 (2020)

    Article  Google Scholar 

  45. Mahendran, A., Vedaldi, A.: Visualizing deep convolutional neural networks using natural pre-images. Int. J. Comput. Vis. 120(3), 233–255 (2016)

    Article  MathSciNet  Google Scholar 

  46. Mousavian, A., Kosecka, J.: Deep convolutional features for image based retrieval and scene categorization. arXiv preprint arXiv:1509.06033 (2015)

  47. Norouzi, M., Fleet, D.J.: Minimal loss hashing for compact binary codes. icml (2011)

  48. Rodrigues, J., Cristo, M., Colonna, J.G.: Deep hashing for multi-label image retrieval: a survey. Artif. Intell. Rev. 2020, 1–47 (2020)

    Google Scholar 

  49. Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  50. Sakr, N.A., ELdesouky, A.I., Arafat, H.: An efficient fast-response content-based image retrieval framework for big data. Comput. Electr. Eng. 54, 522–538 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  53. Shen, Z.-Y., Han, S.-Y., Li-Chen, F., Hsiao, P.-Y., Lau, Y.-C., Chang, S.-J.: Deep convolution neural network with scene-centric and object-centric information for object detection. Image Vis. Comput. 85, 14–25 (2019)

    Article  Google Scholar 

  54. Shi, Z., Ye, Y., Yunpeng, W.: Rank-based pooling for deep convolutional neural networks. Neural Netw. 83, 21–31 (2016)

    Article  Google Scholar 

  55. Tang, J., Li, Z., Zhu, X.: Supervised deep hashing for scalable face image retrieval. Pattern Recognit. 75, 25–32 (2018)

    Article  Google Scholar 

  56. Tang, J., Lin, J., Li, Z., Yang, J.: Discriminative deep quantization hashing for face image retrieval. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 6154–6162 (2018)

    Article  Google Scholar 

  57. Tian, S., Shen, S., Tian, G., Liu, X., Yin, B.: End-to-end deep metric network for visual tracking. Vis. Comput. 2019, 1–14 (2019)

    Google Scholar 

  58. 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 

  59. 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 

  60. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)

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

  62. Xie, W., Jia, X., Shen, L., Yang, M.: Sparse deep feature learning for facial expression recognition. Pattern Recognit. 96, 106966 (2019)

    Article  Google Scholar 

  63. Yang, H., Min, K.: Classification of basic artistic media based on a deep convolutional approach. Vis. Comput. 36(3), 559–578 (2020)

    Article  MathSciNet  Google Scholar 

  64. Yang, H.-F., Lin, K., Chen, C.-S.: Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 437–451 (2017)

    Article  Google Scholar 

  65. Yu, J., Hu, C.-H., Jing, X.-Y., Feng, Y.-J.: Deep metric learning with dynamic margin hard sampling loss for face verification. Signal Image Video Process. 2019, 1–8 (2019)

    Google Scholar 

  66. Yuan, J., Hou, X., Xiao, Y., Cao, D., Guan, W., Nie, L.: Multi-criteria active deep learning for image classification. Knowl. Based Syst. 172, 86–94 (2019)

    Article  Google Scholar 

  67. Zhang, J., Peng, Y.: Ssdh: semi-supervised deep hashing for large scale image retrieval. IEEE Trans. Circuits Syst. Video Technol. 29(1), 212–225 (2017)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  69. Zhang, W., Ji, J., Zhu, J., Li, J., Hua, X., Zhang, B.: Bithash: an efficient bitwise locality sensitive hashing method with applications. Knowl. Based Syst. 97, 40–47 (2016)

    Article  Google Scholar 

  70. Zhang, X., Zhou, L., Bai, X., Luan, X., Luo, J., Hancock, E.R.: Deep supervised hashing using symmetric relative entropy. Pattern Recognit. Lett. 125, 677–683 (2019)

    Article  Google Scholar 

  71. Zhao, F., Huang, Y., Wang, L., Tan, T.: Deep semantic ranking based hashing for multi-label image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1556–1564 (2015)

  72. Zhong, G., Xu, H., Yang, P., Wang, S., Dong, J.: Deep hashing learning networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 2236–2243. IEEE (2016)

  73. Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)

Download references

Acknowledgements

There is no funding source for this research work.

Author information

Authors and Affiliations

  1. Department of Information Science and Technology, Anna University, Chennai, Tamilnadu, India

    P. Arulmozhi & S. Abirami

Authors
  1. P. Arulmozhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Abirami
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Arulmozhi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arulmozhi, P., Abirami, S. DSHPoolF: deep supervised hashing based on selective pool feature map for image retrieval. Vis Comput 37, 2391–2405 (2021). https://doi.org/10.1007/s00371-020-01993-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-020-01993-4

Keywords

Search

Navigation