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Deep regional detail-aware hashing

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Abstract

Deep hashing has been widely studied in recent years. However, most existing methods learn hash codes from the whole image ignoring the region details which are important for representing the precise semantic contents. To tackle this issue, we proposed a deep regional detail-aware hashing (DDAH) to fully utilize this detail information. Specifically, to well reflect the influence of details on the hash codes, we handle these details in a “near-Hamming” space instead of directly fusing them in the original image feature space. Furthermore, based on the framework of DDAH, we can capture the details in the network without the need to partition the original image to different regions manually. To be more specific, considering multiple regions as overlapping subimages, we first design a deep network to learn multiple regional details from these subimages, and then fuse them in the near-Hamming space, which is highly related to Hamming space (i.e., hash code space). Finally, these regional details in the near-Hamming space are directly used to generate hash code of the corresponding image. In addition, a self-similarity loss term is proposed to force these regional details together in the near-Hamming space. In brief, compared to existing hashing methods, the proposed DDAH not only utilizes detail information for hash learning, but also incorporates them into the final hash codes. Extensive experiments on three datasets have indicated that DDAH outperforms most existing models, verifying its effectiveness.

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References

  1. 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 (2018)

    Article  Google Scholar 

  2. Nie, X., Jing, W., Cui, C., Zhang, J., Zhu, L., Yin, Y.: Joint multi-view hashing for large-scale near-duplicate video retrieval. IEEE Trans. Knowl. Data Eng. 32(10), 951–1965 (2019)

    Google Scholar 

  3. Nie, X., Li, X., Chai, Y., Cui, C., Xi, X., Yin, Y.: Robust image fingerprinting based on feature point relationship mining. IEEE Trans. Inf. Forensics Secur. 13(6), 1509–1523 (2018)

    Article  Google Scholar 

  4. Chi, L., Li, B., Zhu, X., Pan, S., Chen, L.: Hashing for adaptive real-time graph stream classification with concept drifts. IEEE Trans. Cybern. 48(5), 1591–1604 (2018). https://doi.org/10.1109/TCYB.2017.2708979

    Article  Google Scholar 

  5. 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)

  6. Shi, Y., Nie, X., Liu, X., Zou, L., Yin, Y.: Supervised adaptive similarity matrix hashing. IEEE Trans. Image Process. 31, 2755–2766 (2022)

    Article  Google Scholar 

  7. Nie, X., Liu, X., Guo, J., Wang, L., Yin, Y.: Supervised discrete multiple-length hashing for image retrieval. IEEE Trans. Big Data (2022)

  8. Shi, Y., Nie, X., Liu, X., Yang, L., Yin, Y.: Zero-shot hashing via asymmetric ratio similarity matrix. IEEE Trans. Knowl Data Eng. (2022)

  9. Nie, X., Zhou, X., Shi, Y., Sun, J., Yin, Y.: Classification-enhancement deep hashing for large-scale video retrieval. Appl. Soft Comput. 109, 107467 (2021)

    Article  Google Scholar 

  10. Li, S., Li, X., Lu, J., Zhou, J.: Self-supervised video hashing via bidirectional transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13549–13558 (2021)

  11. Hu, Y., Liu, M., Su, X., Gao, Z., Nie, L.: Video moment localization via deep cross-modal hashing. IEEE Trans. Image Process. 30, 4667–4677 (2021)

    Article  MathSciNet  Google Scholar 

  12. Yuan, L., Wang, T., Zhang, X., Tay, F.E., Jie, Z., Liu, W., Feng, J.: Central similarity quantization for efficient image and video retrieval. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2020, pp. 3080–3089 (2020)

  13. Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 2415–2421 (2016)

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

  15. Song, J., He, T., Gao, L., Xu, X., Shen, H.T.: Deep region hashing for generic instance search from images. In: AAAI Conference on Artificial Intelligence, pp. 402–409 (2018)

  16. Huang, C.-Q., Yang, S.-M., Pan, Y., Lai, H.-J.: Object-location-aware hashing for multi-label image retrieval via automatic mask learning. IEEE Trans. Image Process 27(9), 4490–4502 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  17. Zhang, X., Lai, H., Feng, J.: Attention-aware deep adversarial hashing for cross-modal retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision—ECCV. Springer International Publishing vol. 2018, pp. 614–629 (2018)

  18. Peng, H., He, J., Chen, S., Wang, Y., Qiao, Y.: Dual-supervised attention network for deep cross-modal hashing. Pattern Recogn. Lett. 128, 333–339 (2019)

    Article  Google Scholar 

  19. Wang, X., Zou, X., Bakker, E.M., Wu, S.: Self-constraining and attention-based hashing network for bit-scalable cross-modal retrieval. Neurocomputing 400, 255–271 (2020)

    Article  Google Scholar 

  20. Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of the 25th International Conference on Very Large Data Bases, pp. 518–529. Morgan Kaufmann Publishers Inc. (1999)

  21. 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. ACM (2004)

  22. Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: IEEE International Conference on Computer Vision, vol. 9, pp. 2130–2137 (2009)

  23. Zhu, L., Shen, J., Xie, L., Cheng, Z.: Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans. Knowl. Data Eng. 29(2), 472–486 (2017)

    Article  Google Scholar 

  24. Zhu, L., Shen, J., Xie, L.: Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans. Knowl. Data Eng. 29(2), 472–486 (2016)

    Article  Google Scholar 

  25. Lin, G., Shen, C., van den Hengel, A.: Supervised hashing using graph cuts and boosted decision trees. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2317–2331 (2015)

    Article  Google Scholar 

  26. Zhu, L., Lu, X., Cheng, Z., Li, J., Zhang, H.: Deep collaborative multi-view hashing for large-scale image search. IEEE Trans. Image Process. 29, 4643–4655 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  27. He, S., Wang, B., Wang, Z., Yang, Y., Shen, F., Huang, Z., Shen, H.T.: Bidirectional discrete matrix factorization hashing for image search. IEEE Trans. Cybern. 50(9), 4157–4168 (2020). https://doi.org/10.1109/TCYB.2019.2941284

    Article  Google Scholar 

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

  29. Liu, Q., Liu, G., Li, L., Yuan, X.-T., Wang, M., Liu, W.: Reversed spectral hashing. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2441–2449 (2018)

    Article  MathSciNet  Google Scholar 

  30. Wang, X.-J., Zhang, L., Jing, F., Ma, W.-Y.: Annosearch: image autoannotation by search. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp. 1483–1490. IEEE (2006)

  31. 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 (2013)

    Article  Google Scholar 

  32. Liu, X., Nie, X., Zeng, W., Cui, C., Zhu, L., Yin, Y.: Fast discrete crossmodal hashing with regressing from semantic labels. In: 2018 ACM Multimedia Conference on Multimedia Conference, pp. 1662–1669. ACM (2018)

  33. Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T.: Fast supervised discrete hashing. IEEE Trans. Pattern Anal. Mach. Intell. 40(2), 490–496 (2017)

    Article  Google Scholar 

  34. Gui, J., Liu, T., Sun, Z., Tao, D., Tan, T.: Supervised discrete hashing with relaxation. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 608–617 (2016)

    Article  Google Scholar 

  35. Gui, J., Li, P.: R2sdh: robust rotated supervised discrete hashing. In: 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018. Association for Computing Machinery, pp. 1485–1493 (2018)

  36. Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels. In: International Joint Conference on Artificial Intelligence (2016)

  37. Jiang, Q.-Y., Li, W.-J.: Asymmetric deep supervised hashing. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

  38. Cui, H., Zhu, L., Li, J., Yang, Y., Nie, L.: Scalable deep hashing for large-scale social image retrieval. IEEE Trans. Image Process. 29, 1271–1284 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  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. Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: Advances in Neural Information Processing Systems, pp. 2479–2488 (2017)

  41. 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)

  42. 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 

  43. Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)

  44. Hjelm, R.D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., Bengio, Y.L. Learning deep representations by mutual information estimation and maximization. In: International Conference on Learning Representations (2019)

  45. Shen, Y., Liu, L., Shao, L., Song, J.: Deep binaries: encoding semantic-rich cues for efficient textual-visual cross retrieval. In: IEEE International Conference on Computer Vision, pp. 4117–4126 (2017)

  46. Jin, L., Shu, X., Li, K., Li, Z., Qi, G.-J., Tang, J.: Deep ordinal hashing with spatial attention. IEEE Trans. Image Process. 28(5), 2173–2186 (2019)

    Article  MathSciNet  Google Scholar 

  47. Xiang, X., Zhang, Y., Jin, L., Li, Z., Tang, J.: Sub-region localized hashing for fine-grained image retrieval. IEEE Trans. Image Process. 31, 314–326 (2021)

    Article  Google Scholar 

  48. 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)

  49. Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Computer Science Department, University of Toronto, Tech. Rep., vol. (2009)

  50. 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, p. 48. ACM (2009)

  51. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp. 740–755. Springer (2014)

  52. Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. In: Advances in Neural Information Processing Systems, pp. 1509–1517 (2009)

  53. 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 

  54. Luo, X., Nie, L., He, X., Wu, Y., Chen, Z.-D., Xu, X.-S.: Fast scalable supervised hashing. In: The 41st International ACM SIGIR Conference on Research Development in Information Retrieval, pp. 735–744. ACM (2018)

  55. Hoe, J.T., Ng, K.W., Zhang, T., Chan, C.S., Song, Y.-Z., Xiang, T.: One loss for all: deep hashing with a single cosine similarity based learning objective. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

  56. Zhou, Q., Nie, X., Shi, Y., Liu, X., Yin, Y.: Deep multi-region hashing. In: ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1938–1942 (2020)

  57. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  58. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (62176141, 62102235), Shandong Provincial Natural Science Foundation for Distinguished Young Scholars (ZR2021JQ26), Shandong Provincial Natural Science Foundation (ZR2020QF029), Taishan Scholar Project of Shandong Province (tsqn202103088).

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China

    Letian Wang, Yuling Ma, Jie Guo, Xiushan Nie & Yilong Yin

  2. School of Software, Shandong University, Jinan, 250100, China

    Quan Zhou

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  1. Letian Wang
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  2. Quan Zhou
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  3. Yuling Ma
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  4. Jie Guo
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  5. Xiushan Nie
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Contributions

LW and QZ are mainly responsible for the construction of the framework and the writing of the code. YM and JG are mainly responsible for the writing of related modules. XN and YY are mainly responsible for the guidance and macro control of ideas. All authors reviewed the manuscript

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Correspondence to Yuling Ma or Xiushan Nie.

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Wang, L., Zhou, Q., Ma, Y. et al. Deep regional detail-aware hashing. Multimedia Systems 29, 153–166 (2023). https://doi.org/10.1007/s00530-022-00988-6

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