Abstract
In the task of image retrieval, the nearest neighbor algorithm is widely used because of its high efficiency, where hashing algorithm is one of typical representatives. In recent years, with the development of deep convolutional neural networks, there have been many deep hashing algorithms for image retrieval. This paper proposes a new deep hashing algorithm that adds a hash layer to the image classification networks to obtain hash codes. A constraint item be added to the classification loss function, which is used to pick out some important nodes from the hash layer, and these selected nodes representing the picture are encoded. Compared with other existing algorithms, the performance of our algorithm has a certain improvement.
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Gong, Y., Lazebnik, S., Gordo, A., et al.: 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, J., Liu, W., Chang, S.F.: Scalable similarity search with optimized Kernel hashing. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1129–1138. ACM (2010)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: VLDB, vol. 99, no. 6, pp. 518–529 (1999)
Datar, M., Immorlica, N., Indyk, P., et al.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proceedings of the Twentieth Annual Symposium on Computational Geometry, pp. 253–262. ACM (2004)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2130–2137. IEEE (2009)
Mu, Y., Yan, S.: Non-metric locality-sensitive hashing. In: AAAI, pp. 539–544 (2010)
Ji, J., Li, J., Yan, S., et al.: Super-bit locality-sensitive hashing. In: Advances in Neural Information Processing Systems, pp. 108–116 (2012)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: Advances in Neural Information Processing Systems, pp. 1753–1760 (2009)
Jiang, Q.Y., Li, W.J.: Scalable graph hashing with feature transformation. In: IJCAI, pp. 2248–2254 (2015)
Liu, H., Ji, R., Wu, Y., et al.: Towards optimal binary code learning via ordinal embedding. In: AAAI, pp. 1258–1265 (2016)
Liu, W., Wang, J., Ji, R., et al.: Supervised hashing with Kernels. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2074–2081. IEEE (2012)
Zhang, P., Zhang, W., Li, W.J., et al.: Supervised hashing with latent factor models. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 173–182. ACM (2014)
Shen, F., Shen, C., Liu, W., et al.: Supervised discrete hashing. In: CVPR, vol. 2, no. 3, p. 5 (2015)
Shi, X., Xing, F., Xu, K., Sapkota, M., Yang, L.: Asymmetric discrete graph hashing. In: AAAI, pp. 2541–2547 (2017)
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)
Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)
Sun, Y., et al.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)
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)
Xia, R., Pan, Y., Lai, H., et al.: Supervised hashing for image retrieval via image representation learning. In: AAAI, vol. 1, p. 2 (2014)
Lai, H., Pan, Y., Liu, Y., et al.: Simultaneous feature learning and hash coding with deep neural networks. arXiv preprint arXiv:1504.03410 (2015)
Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels. arXiv preprint arXiv:1511.03855 (2015)
Zhu, H., Long, M., Wang, J., et al.: Deep hashing network for efficient similarity retrieval. In: AAAI, pp. 2415–2421 (2016)
Liu, H., Wang, R., Shan, S., et al.: Deep supervised hashing for fast image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2064–2072 (2016)
Shen, F., Gao, X., Liu, L., et al.: Deep asymmetric pairwise hashing. In: Proceedings of the 2017 ACM on Multimedia Conference, pp. 1522–1530. ACM (2017)
Li, Q., Sun, Z., He, R., et al.: Deep supervised discrete hashing. In: Advances in Neural Information Processing Systems, pp. 2479–2488 (2017)
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 (2018)
Song, J.: Binary generative adversarial networks for image retrieval. arXiv preprint arXiv:1708.04150 (2017)
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This work is supported in part by the National Natural Science Foundation of China under Grants 61301112 and 61422111.
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Zheng, H., Ma, R., An, P., Li, T. (2019). Feature-Selecting Based Hashing via Deep Convolutional Neural Networks. In: Zhai, G., Zhou, J., An, P., Yang, X. (eds) Digital TV and Multimedia Communication. IFTC 2018. Communications in Computer and Information Science, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-13-8138-6_12
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DOI: https://doi.org/10.1007/978-981-13-8138-6_12
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