Abstract
Recently, some point-wise hash learning methods such as CSQ and DPN adapted “hash centers” as the global similarity label for each category and force the hash codes of the images with the same category to get closed to their corresponding hash centers. Although they outperformed other pairwise/triplet hashing methods, they assign hash centers to each class randomly and result in a sub-optimal performance because of ignoring the semantic relationship between categories, which means that they ignore the fact that the Hamming distance between the hash centers corresponding to two semantically similar classes should be smaller than the Hamming distance between the hash centers corresponding to two semantically dissimilar classes. To solve the above problem and generate well-separated and semantic hash centers, in this paper, we propose an optimization approach which aims at generating hash centers not only with semantic category information but also distinguished from each other. Specifically, we adopt the weight of last fully-connected layer in ResNet-50 model as category features to help inject semantic information into the generation of hash centers and try to maximize the expectation of the Hamming distance between each two hash centers. With the hash centers corresponding to each image category, we propose two effective loss functions to learn deep hashing function. Importantly, extensive experiments show that our proposed hash centers and training method outperform the state-of-the-art hash models on three image retrieval datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alex, K., Ilya, S., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS-12), pp. 1097–1105 (2012)
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 (CVPR-18), pp. 1229–1237 (2018)
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 (ICCV-17), pp. 5608–5617 (2017)
Fan, L., Ng, K., Ju, C., Zhang, T., Chan, C.S.: Deep polarized network for supervised learning of accurate binary hashing codes. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence (IJCAI-20), pp. 825–831 (2020)
Gong, Y., Kumar, S., Verma, V., Lazebnik, S.: Angular quantization-based binary codes for fast similarity search. In: Advances in Neural Information Processing Systems (NIPS-12), pp. 1196–1204 (2012)
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)
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 (CVPR-16), pp. 770–778 (2016)
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 (NIPS-21) (2021)
Karaman, S., Lin, X., Hu, X., Chang, S.F.: Unsupervised rank-preserving hashing for large-scale image retrieval. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval, pp. 192–196 (2019)
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV-13), pp. 554–561 (2013)
Li, Q., Sun, Z., He, R., Tan, T.: Deep supervised discrete hashing. In: Advances in Neural Information Processing Systems (NIPS-17), pp. 2479–2488 (2017)
Li, W., Wang, S., Kang, W.: Feature learning based deep supervised hashing with pairwise labels. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 1711–1717 (2016)
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)
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 (CVPR-16), pp. 2064–2072 (2016)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NIPS-19), pp. 8024–8035. Curran Associates, Inc. (2019)
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Shen, F., Shen, C., Liu, W., Tao Shen, H.: Supervised discrete hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-15), pp. 37–45 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Su, S., Zhang, C., Han, K., Tian, Y.: Greedy hash: towards fast optimization for accurate hash coding in CNN. In: Advances in Neural Information Processing Systems (NIPS-18), pp. 806–815 (2018)
Van Horn, G., et al.: Building a bird recognition app and large scale dataset with citizen scientists: the fine print in fine-grained dataset collection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-15), pp. 595–604 (2015)
Wang, X., Shi, Y., Kitani, K.M.: Deep supervised hashing with triplet labels. In: Asian Conference on Computer Vision (ACCV-16), pp. 70–84 (2016)
Wu, B., Ghanem, B.: \(\ell _p\)-box ADMM: a versatile framework for integer programming. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1695–1708 (2019)
Xia, R., Pan, Y., Lai, H., Liu, C., Yan, S.: Supervised hashing for image retrieval via image representation learning. In: Proceedings of the AAAI conference on Artificial Intelligence (AAAI-14), pp. 2156–2162 (2014)
Yair, W., Antonio, T., Robert, F.: Spectral hashing. In: Advances in Neural Information Processing Systems (NIPS-08), pp. 1753–1760 (2008)
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)
Yuan, L., et al.: Central similarity quantization for efficient image and video retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-20), pp. 3083–3092 (2020)
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)
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: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9291–9298 (2019)
Zhou, C., et al.: Angular deep supervised hashing for image retrieval. IEEE Access 7, 127521–127532 (2019)
Zhu, H., Long, M., Wang, J., Cao, Y.: Deep hashing network for efficient similarity retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-16), pp. 2415–2421 (2016)
Zieba, M., Semberecki, P., El-Gaaly, T., Trzcinski, T.: BinGAN: learning compact binary descriptors with a regularized GAN. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Acknowledgements
This work was supported in part by the National Science Foundation of China under Grant U1811262, Grant 61772567.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, L., Pan, Y., Lai, H., Yin, J. (2023). Image Retrieval with Well-Separated Semantic Hash Centers. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13846. Springer, Cham. https://doi.org/10.1007/978-3-031-26351-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-26351-4_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-26350-7
Online ISBN: 978-3-031-26351-4
eBook Packages: Computer ScienceComputer Science (R0)