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ExchNet: A Unified Hashing Network for Large-Scale Fine-Grained Image Retrieval

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12348)

Abstract

Retrieving content relevant images from a large-scale fine-grained dataset could suffer from intolerably slow query speed and highly redundant storage cost, due to high-dimensional real-valued embeddings which aim to distinguish subtle visual differences of fine-grained objects. In this paper, we study the novel fine-grained hashing topic to generate compact binary codes for fine-grained images, leveraging the search and storage efficiency of hash learning to alleviate the aforementioned problems. Specifically, we propose a unified end-to-end trainable network, termed as ExchNet. Based on attention mechanisms and proposed attention constraints, ExchNet can firstly obtain both local and global features to represent object parts and the whole fine-grained objects, respectively. Furthermore, to ensure the discriminative ability and semantic meaning’s consistency of these part-level features across images, we design a local feature alignment approach by performing a feature exchanging operation. Later, an alternating learning algorithm is employed to optimize the whole ExchNet and then generate the final binary hash codes. Validated by extensive experiments, our ExchNet consistently outperforms state-of-the-art generic hashing methods on five fine-grained datasets. Moreover, compared with other approximate nearest neighbor methods, ExchNet achieves the best speed-up and storage reduction, revealing its efficiency and practicality.

Keywords

Fine-Grained Image Retrieval Learning to hash Feature alignment Large-scale image search 

Notes

Acknowledgements

Quan Cui’s contribution was made when he was an intern at Megvii Research Nanjing. This research was supported by the National Key Research and Development Program of China under Grant 2017YFA0700800 and “111” Program B13022. Qing-Yuan Jiang and Wu-Jun Li were supported by the NSFC-NRF Joint Research Project (No. 61861146001).

Supplementary material

504435_1_En_12_MOESM1_ESM.pdf (120 kb)
Supplementary material 1 (pdf 119 KB)

References

  1. 1.
    Bentley, J.L.: Multidimensional binary search trees used for associative searching. ACM Commun. 18(9), 509–517 (1975)CrossRefGoogle Scholar
  2. 2.
    Cakir, F., He, K., Sclaroff, S.: Hashing with binary matrix pursuit. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 344–361. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01228-1_21CrossRefGoogle Scholar
  3. 3.
    Cao, Y., Long, M., Liu, B., Wang, J.: Deep cauchy hashing for hamming space retrieval. In: CVPR, pp. 1229–1237 (2018)Google Scholar
  4. 4.
    Cao, Z., Long, M., Wang, J., Yu, P.S.: HashNet: deep learning to hash by continuation. In: ICCV, pp. 5609–5618 (2017)Google Scholar
  5. 5.
    Chen, J., Wang, Y., Qin, J., Liu, L., Shao, L.: Fast person re-identification via cross-camera semantic binary transformation. In: CVPR, pp. 5330–5339 (2017)Google Scholar
  6. 6.
    Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: SoCG, pp. 253–262 (2004)Google Scholar
  7. 7.
    Dizaji, K.G., Zheng, F., Sadoughi, N., Yang, Y., Deng, C., Huang, H.: Unsupervised deep generative adversarial hashing network. In: CVPR, pp. 3664–3673 (2018)Google Scholar
  8. 8.
    Dolatshah, M., Hadian, A., Minaei-Bidgoli, B.: Ball*-tree: efficient spatial indexing for constrained nearest-neighbor search in metric spaces. CoRR abs/1511.00628 (2015)Google Scholar
  9. 9.
    Fu, J., Zheng, H., Mei, T.: Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: CVPR, pp. 4438–4446 (2017)Google Scholar
  10. 10.
    Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: CVPR, pp. 817–824 (2011)Google Scholar
  11. 11.
    Horn, G.V., et al.: Building a bird recognition app and large scale dataset with citizen scientists: the fine print in fine-grained dataset collection. In: CVPR, pp. 595–604 (2015)Google Scholar
  12. 12.
    Hou, S., Feng, Y., Wang, Z.: VegFru: a domain-specific dataset for fine-grained visual categorization. In: ICCV, pp. 541–549 (2017)Google Scholar
  13. 13.
    Jégou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE TPAMI 33(1), 117–128 (2011)CrossRefGoogle Scholar
  14. 14.
    Jiang, Q.Y., Li, W.J.: Asymmetric deep supervised hashing. In: AAAI, pp. 3342–3349 (2018)Google Scholar
  15. 15.
    Li, P., Xie, J., Wang, Q., Gao, Z.: Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In: CVPR, pp. 947–955 (2018)Google Scholar
  16. 16.
    Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: NeurIPS, pp. 2482–2491 (2017)Google Scholar
  17. 17.
    Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels. In: IJCAI, pp. 1711–1717 (2016)Google Scholar
  18. 18.
    Lin, J., Li, Z., Tang, J.: Discriminative deep hashing for scalable face image retrieval. In: IJCAI, pp. 2266–2272 (2017)Google Scholar
  19. 19.
    Lin, K., Yang, F., Wang, Q., Piramuthu, R.: Adversarial learning for fine-grained image search. In: ICME, pp. 490–495 (2019)Google Scholar
  20. 20.
    Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: CVPR, pp. 1449–1457 (2015)Google Scholar
  21. 21.
    Liong, V.E., Lu, J., Wang, G., Moulin, P., Zhou, J.: Deep hashing for compact binary codes learning. In: CVPR, pp. 2475–2483 (2015)Google Scholar
  22. 22.
    Liu, H., Wang, R., Shan, S., Chen, X.: Deep supervised hashing for fast image retrieval. In: CVPR, pp. 2064–2072 (2016)Google Scholar
  23. 23.
    Bossard, L., Guillaumin, M., Van Gool, L.: Food-101 – mining discriminative components with random forests. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 446–461. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10599-4_29CrossRefGoogle Scholar
  24. 24.
    Maji, S., Rahtu, E., Kannala, J., Blaschko, M.B., Vedaldi, A.: Fine-grained visual classification of aircraft. CoRR abs/1306.5151 (2013)Google Scholar
  25. 25.
    Neyshabur, B., Srebro, N., Salakhutdinov, R.R., Makarychev, Y., Yadollahpour, P.: The power of asymmetry in binary hashing. In: NeurIPS, pp. 2823–2831 (2013)Google Scholar
  26. 26.
    Pang, C., Li, H., Cherian, A., Yao, H.: Part-based fine-grained bird image retrieval respecting species correlation. In: ICIP, pp. 2896–2900 (2017)Google Scholar
  27. 27.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)Google Scholar
  28. 28.
    Shen, F., Shen, C., Liu, W., Shen, H.T.: Supervised discrete hashing. In: CVPR, pp. 37–45 (2015)Google Scholar
  29. 29.
    Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)Google Scholar
  30. 30.
    Wang, G., Hu, Q., Cheng, J., Hou, Z.: Semi-supervised generative adversarial hashing for image retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 491–507. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01267-0_29CrossRefGoogle Scholar
  31. 31.
    Wei, X.S., Luo, J.H., Wu, J., Zhou, Z.H.: Selective convolutional descriptor aggregation for fine-grained image retrieval. IEEE TIP 26(6), 2868–2881 (2017)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Wei, X.S., Wang, P., Liu, L., Shen, C., Wu, J.: Piecewise classifier mappings: learning fine-grained learners for novel categories with few examples. IEEE TIP 28(12), 6116–6125 (2019)MathSciNetzbMATHGoogle Scholar
  33. 33.
    Wei, X.S., Xie, C.W., Wu, J., Shen, C.: Mask-CNN: localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recogn. 76, 704–714 (2018)CrossRefGoogle Scholar
  34. 34.
    Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NeurIPS, pp. 1753–1760 (2008)Google Scholar
  35. 35.
    Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: AAAI, pp. 2156–2162 (2014)Google Scholar
  36. 36.
    Xie, L., Wang, J., Zhang, B., Tian, Q.: Fine-grained image search. IEEE Trans. Multimed. 17(5), 636–647 (2015)CrossRefGoogle Scholar
  37. 37.
    Yang, Z., Luo, T., Wang, D., Hu, Z., Gao, J., Wang, L.: Learning to navigate for fine-grained classification. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 438–454. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01264-9_26CrossRefGoogle Scholar
  38. 38.
    Yuan, X., Ren, L., Lu, J., Zhou, J.: Relaxation-free deep hashing via policy gradient. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 141–157. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01225-0_9CrossRefGoogle Scholar
  39. 39.
    Zhang, J., et al.: Generative domain-migration hashing for sketch-to-image retrieval. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 304–321. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01216-8_19CrossRefGoogle Scholar
  40. 40.
    Zheng, H., Fu, J., Mei, T., Luo, J.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: CVPR, pp. 5209–5217 (2017)Google Scholar
  41. 41.
    Zheng, X., Ji, R., Sun, X., Wu, Y., Huang, F., Yang, Y.: Centralized ranking loss with weakly supervised localization for fine-grained object retrieval. In: IJCAI, pp. 1226–1233 (2018)Google Scholar
  42. 42.
    Zheng, X., Ji, R., Sun, X., Zhang, B., Wu, Y., Huang, F.: Towards optimal fine grained retrieval via decorrelated centralized loss with normalize-scale layer. In: AAAI, vol. 33, pp. 9291–9298 (2019)Google Scholar
  43. 43.
    Zhu, F., Kong, X., Zheng, L., Fu, H., Tian, Q.: Part-based deep hashing for large-scale person re-identification. IEEE TIP 26(10), 4806–4817 (2017)MathSciNetGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Graduate School of IPSWaseda UniversityFukuokaJapan
  2. 2.National Key Laboratory for Novel Software Technology, Department of Computer Science and TechnologyNanjing UniversityNanjingChina
  3. 3.Megvii Research NanjingMegvii TechnologyNanjingChina

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