激光与光电子学进展, 2018, 55 (10): 101002, 网络出版: 2018-10-14   

基于哈希算法及生成对抗网络的图像检索 下载: 855次

Image Retrieval Based on Hash Method and Generative Adversarial Networks
作者单位
辽宁工程技术大学电子与信息工程学院, 辽宁 葫芦岛 125105
引用该论文

彭晏飞, 武宏, 訾玲玲. 基于哈希算法及生成对抗网络的图像检索[J]. 激光与光电子学进展, 2018, 55(10): 101002.

Peng Yanfei, Wu Hong, Zi Lingling. Image Retrieval Based on Hash Method and Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101002.

参考文献

[1] Cao Z, Long M, Wang J, et al. HashNet: deep learning to Hash by continuation[C]∥IEEE International Conference on Computer Vision, 2017: 5609-5618.

[2] 刘兵, 张鸿. 基于卷积神经网络和流形排序的图像检索算法[J]. 计算机应用, 2016, 36(2): 531-534, 540.

    Liu B, Zhang H. Image retrieval algorithm based on convolutional neural network and manifold ranking[J]. Journal of Computer Applications, 2016, 36(2): 531-534, 540.

[3] 刘冶, 潘炎, 夏榕楷, 等. FP-CNNH: 一种基于深度卷积神经网络的快速图像哈希算法[J]. 计算机科学, 2016, 43(9): 39-46, 51.

    Liu Y, Pan Y, Xia R K, et al. FP-CNNH: a fast image hashing algorithm based on deep convolutional neural network[J]. Computer Science, 2016, 43(9): 39-46, 51.

[4] 彭天强, 栗芳. 基于深度卷积神经网络和二进制哈希学习的图像检索方法[J]. 电子与信息学报, 2016, 38(8): 2068-2075.

    Peng T Q, Li F. Image retrieval based on deep convolutional neural networks and binary hashing learning[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2068-2075.

[5] 李武军, 周志华. 大数据哈希学习: 现状与趋势[J]. 科学通报, 2015, 60(Z1): 485-490.

    Li W J, Zhou Z H. Learning to hash for big data: current status and future trends[J]. Chinese Science Bulletin, 2015, 60(Z1): 485-490.

[6] Kan M N, Xu D, Shan S G, et al. Semisupervised hashing via kernel hyperplane learning for scalable image search[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(4): 704-713.

[7] 王彦超, 郭静博, 周丽宴. 基于数据降维与对称二值模式的图像Hash算法[J]. 激光与光电子学进展, 2017, 54(2): 021004.

    Wang Y C, Guo J B, Zhou L Y. Image hash algorithm based on data dimension reduction and symmetric binary pattern[J]. Laser & Optoelectronics Progress, 2017, 54(2): 021004.

[8] Norouzi M, Fleet D J. Minimal loss hashing for compact binary codes[C]∥Proceedings of the 28th International Conference on Machine Learning. 2011: 353-360.

[9] Strecha C, Bronstein A M, Bronstein M M, et al. LDAHash: improved matching with smaller descriptors[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(1): 66-78.

[10] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014: 2672-2680.

[11] Chen X, Duan Y, Houthooft R, et al. Infogan: interpretable representation learning by information maximizing generative adversarial nets[C]∥Advances in Neural Information Processing Systems, 2016: 2172-2180.

[12] Larsen A B L, Snderby S K, Larochelle H, et al. Autoencoding beyond pixels using a learned similarity metric[C]∥International Conference on Machine Learning, 2016: 1558-1566.

[13] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv, 2015: 1511. 06434.

[14] Oquab M, Bottou L, Laptev I, et al. Learning and transferring mid-level image representations using convolutional neural networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1717-1724.

[15] 侯聪聪, 何宇清, 姜晓恒, 等. 基于二分支卷积单元的深度卷积神经网络[J]. 激光与光电子学进展, 2018, 55(2): 021005.

    Hou C C, He Y Q, Jiang X H, et al. Deep convolutional neural network based on two-stream convolutional unit[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021005.

[16] 李素梅, 雷国庆, 范如. 基于卷积神经网络的深度图超分辨率重建[J]. 光学学报, 2017, 37(12): 1210002.

    Li S M, Lei G Q, Fan R. Depth map super-resolution reconstruction based on convolutional neural networks[J]. Acta Optica Sinica, 2017, 37(12): 1210002.

[17] Zhu H, Long M, Wang J, et al. Deep Hashing network for efficient similarity retrieval[C]∥Thirtieth AAAI Conference on Artificial Intelligence, 2016: 2415-2421.

彭晏飞, 武宏, 訾玲玲. 基于哈希算法及生成对抗网络的图像检索[J]. 激光与光电子学进展, 2018, 55(10): 101002. Peng Yanfei, Wu Hong, Zi Lingling. Image Retrieval Based on Hash Method and Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2018, 55(10): 101002.

本文已被 2 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!