Abstract
At present, hashing algorithm has been combined with deep learning to accelerate image retrieval. Against this background, there are many ways to construct hashing, but most of the methods do not show excellent performance in reducing semantic loss. At the same time, the vast majority of cases that adopt hashing algorithm and obtain successful cases involve the identification model requiring labels. So we propose a high precision with the combination of self-learning hash algorithm (HPSLH) to conduct experiments, the algorithm can not only through the analysis of the data itself, and construct a set of false label, then using the data from the identification model of deep learning can also avoid enormous semantic loss in the process of our hash. Through experiments on traditional datasets, this method can achieve the desired goal.
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References
Salakhutdinov, R., Hinton, G.: Semantic hashing. Int. J. Approx. Reason. 50(7), 969–978 (2009)
Xia, R., et al.: Supervised hashing for image retrieval via image representation learning (2014)
Lai, H., et al.: Simultaneous feature learning and hash coding with deep neural networks, pp. 3270–3278 (2015)
Zhao, F., et al.: Deep semantic ranking based hashing for multi-label image retrieval. In: Computer Vision and Pattern Recognition, pp. 1556–1564. IEEE (2015)
Lin, K., et al.: Deep learning of binary hash codes for fast image retrieval. In: Computer Vision and Pattern Recognition Workshops, pp. 27–35. IEEE (2015)
Zhang, R., et al.: Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans. Image Process. 24(12), 4766–4779 (2015)
Liu, H., et al.: Deep supervised hashing for fast image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition IEEE Computer Society, pp. 2064–2072 (2016)
Zhu, H., et al.: Deep hashing network for efficient similarity retrieval. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 2415–2421. AAAI Press (2016)
Li, W.J., Wang, S., Kang, W.C.: Feature learning based deep supervised hashing with pairwise labels, pp. 1711–1717 (2015)
Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998)
Heo, J.P., et al.: Spherical hashing: binary code embedding with hyperspheres. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2304–2316 (2015)
Gong, Y., 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 (2013)
Zhang, D., et al.: Self-taught hashing for fast similarity search. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 18–25. ACM (2010)
Liu, Y., et al.: FP-CNNH: a fast image hashing algorithm based on deep convolutional neural network. Computer Science (2016)
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)
Lin, K., Yang, H.-F., Hsiao, J.-H., Chen, C.-S.: Deeplearning of binary hash codes for fast image retrieval. In: CVPR Workshops, pp. 27–35 (2015)
Lai, H., Pan, Y., Liu, Y., Shuicheng, Y.: Simultaneous feature learning and hash coding with deep neuralnetworks. In: CVPR, pp. 3270–3278 (2015)
Dong, Z., Jia, S., Wu, T., Pei, M.: Face video retrieval via deep learning of binary hash representations. In: 12th IEEE Transactions on Multimedia, pp. 3471–3477. AAAI (2016). XX(X), December 2014
Guo, J., Zhang, S., Li, J.: Hash learning with convolutional neural networks for semantic based image retrieval. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) PAKDD 2016. LNCS (LNAI), vol. 9651, pp. 227–238. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31753-3_19
Gardner, M.W., Dorling, S.: Artificial neural networks (the multilayer perceptron)ła review of applications in the atmospheric sciences. Atmos. Environ. 32(14), 2627–2636 (1998)
Sinyavskiy, O., Polonichko, V.: Apparatus and methods for backward propagation of errors in a spiking neuron network. US9489623 (2016)
Zhou, K., et al.: Deep self-taught hashing for image retrieval, pp. 1215–1218 (2015)
He, K., Wen, F., Sun, J.: K-means hashing: an affinity-preserving quantization method for learning binary compact codes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2938–2945. IEEE Computer Society (2013)
Heo, J., Lee, Y., He, J., Chang, S.F., Yoon, S.: Spherical hashing. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2957–2964 (2012)
Lai, H., et al.: Sparse learning-to-rank via an efficient primal- dual algorithm. IEEE Trans. Comput. 62(6), 1221–1233 (2013)
Acknowledgement
Project supported by the National Natural Science Foundation (61502155, 61772180); Education cooperation and cooperative education project (201701003076); Research start-up fund of Hubei university of technology (BSQD029); University student innovation and entrepreneurship project of Hubei university of technology (201710500047).
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Fu, Jr., Yan, Ly., Yuan, L., Zhou, Y., Zhang, Hx., Wang, Cz. (2018). High Precision Self-learning Hashing for Image Retrieval. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_57
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DOI: https://doi.org/10.1007/978-981-13-2203-7_57
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