References
[1] A. Andoni and P. Indyk, “Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions,” Communications of the ACM, vol. 51, no. 1, p. 117, 2008.
[2] B. Kulis and K. Grauman, “Kernelized locality-sensitive hashing for scalable image search.,” in ICCV, vol. 9, pp. 2130–2137, 2009.
[3] Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, “Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval,” IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 12, pp. 2916–2929, 2012.
[4] W. Kong andW.-J. Li, “Isotropic hashing,” in Advances in neural information processing systems, pp. 1646–1654, 2012.
[5] W. Liu, J. Wang, R. Ji, Y.-G. Jiang, and S.-F. Chang, “Supervised hashing with kernels,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2074–2081, IEEE, 2012.
[6] M. Rastegari, J. Choi, S. Fakhraei, D. Hal, and L. Davis, “Predictable dual-view hashing,” in International Conference on Machine Learning, pp. 1328–1336, 2013.
[7] K. He, F. Wen, and J. Sun, “K-means hashing: An affinity-preserving quantization method for learning binary compact codes,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2938–2945, 2013.
[8] G. Lin, C. Shen, Q. Shi, A. Van den Hengel, and D. Suter, “Fast supervised hashing with decision trees for high-dimensional data,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1963–1970, 2014.
[9] F. Shen, C. Shen, W. Liu, and H. Tao Shen, “Supervised discrete hashing,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 37–45, 2015.
[10] W.-C. Kang, W.-J. Li, and Z.-H. Zhou, “Column sampling based discrete supervised hashing,” in Thirtieth AAAI conference on artificial intelligence, 2016.
[11] Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, “Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval,” IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 12, pp. 2916–2929, 2012.
[12] W. Kong and W.-J. Li, “Isotropic hashing,” in Advances in neural information processing systems, pp. 1646–1654, 2012.
[13] J. Wang, W. Liu, S. Kumar, and S.-F. Chang, “Learning to hash for indexing big data — a survey,” Proceedings of the IEEE, vol. 104, no. 1, pp. 34–57, 2015.
[14] W.-J. Li, S. Wang, and W.-C. Kang, “Feature learning based deep supervised hashing with pairwise labels,” arXiv preprint arXiv:1511.03855, 2015.
[15] X. Wang, Y. Shi, and K. M. Kitani, “Deep supervised hashing with triplet labels,” in Asian conference on computer vision, pp. 70–84, Springer, 2016.
[16] P. Zhang, W. Zhang, W.-J. Li, and M. Guo, “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.
[17] H. Zhu, M. Long, J. Wang, and Y. Cao, “Deep hashing network for efficient similarity retrieval,” in Thirtieth AAAI Conference on Artificial Intelligence, 2016
[18] Q. Li, Z. Sun, R. He, and T. Tan, “Deep supervised discrete hashing,” in Advances in neural information processing systems, pp. 2482–2491, 2017.