基于生成对抗网络的序列交叉熵哈希

doi:10.13229/j.cnki.jdxbgxb.20220048

摘要/Abstract

摘要：

为了快速响应遥感图像的近邻检索请求，提出了基于生成对抗网络的序列交叉熵哈希（GANOCH）。首先，建立了基于交叉熵的序列保持目标函数，确保任意三元组样本在不同空间内的序列相似性关系具有相同的概率分布，增强了序列保持约束性。然后，为解决优化过程中的非确定性多项式（NP）难问题，对目标函数中的离散编码进行了连续化松驰处理，并建立了基于三元序列交叉熵的量化损失函数，以减少由连续化过程引起的误差。同时，构造了基于类别标签矩阵的监督生成对抗网络，以增广训练数据集，避免由数据不足或不均衡导致深度网络发生过拟合。最后，在UCMD、SAT4和SAT6遥感数据集上设置了近邻检索性能对比实验，结果表明，基于生成对抗网络的序列交叉熵哈希的近邻检索性能较优。

关键词: 计算机应用技术, 哈希算法, 交叉熵, 序列保持

Abstract:

To fast respond the large scale RS image search task， the deep generative adversarial learning ordinal cross entropy Hashing （GANOCH） was proposed. Firstly， the ordinal relation preserving objective function was established based on cross entropy， then the triplet similarity relationship owned the same probability distribution in different spaces. As a result， the ability of preserving ordinal relation is enhanced. Then， to fix the NP hard problem， the discrete binary encoding was relaxed to the continuous representation. Furthermore， the quantization loss function was designed based on triplet ordinal cross entropy and the loss caused by the continuous mechanism was minimized. Finally， a supervised deep generative adversarial network was established to synthesize RS image dataset according to both label matrix and random noise. Thus， the over fitting problem is avoided. The final ANN search comparative experiments were conducted on three large scale datasets， and the proposed GANOCH achieved the best performance.

Key words: computer application technology, Hashing algorithm, cross entropy, ordinal relation preserving

中图分类号:

TP391.41

王振,杨宵晗,吴楠楠,李国坤,冯创. 基于生成对抗网络的序列交叉熵哈希[J]. 吉林大学学报(工学版), 2023, 53(12): 3536-3546.

Zhen WANG,Xiao-han YANG,Nan-nan WU,Guo-kun LI,Chuang FENG. Ordinal cross entropy Hashing based on generative adversarial network[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3536-3546.

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参考文献 26

1	Shan Xue, Liu Ping-ping, Wang Yi-fan, et al. Deep Hashing using proxy loss on remote sensing image retrieval[J]. Remote Sensing, 2021, 13(15): No. 2924.
2	Shan Xue, Liu Ping-ping, Gou Gui-xia, et al. Deep hash remote sensing image retrieval with hard probability sampling[J]. Remote Sensing, 2020, 12(17): No. 2789.
3	冯小康, 彭延国, 崔江涛, 等. 基于最优排序局部敏感哈希索引[J]. 计算机学报, 2020, 43(5): 930-947.
	Feng Xiao-kang, Peng Yan-guo, Cui Jiang-tao, et al. Locality sensitive Hashing index based on optimal linear order[J]. Chinese Journal of Computers, 2020, 43(5): 930-947.
4	赵宏伟, 王振, 杨文迪, 等. 熵选择多重二进制编码[J].吉林大学学报: 工学版, 2017, 47(1): 218-226.
	Zhao Hong-wei, Wang Zhen, Yang Wen-di, et al. Multiple binary codes based on entropy selection[J]. Journal of Jilin University: Engineering and Technology Edition, 2017, 47(1): 218-226.
5	Hou Yong, Wang Qing-jun. Research and improvement of content based image retrieval framework[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32(12): No.1850043.
6	Wang J, Liu W, Sanjiv K, et al. Learning to hash for indexing big data—a survey[J]. Proceedings of the IEEE, 2016, 104(1): 34-57.
7	Wang Jing-dong, Zhang Ting, Song Jing-kuan, et al. A survey on learning to hash[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2018, 40(4): 769-790.
8	Mayur D, Nicole I, Piotr I, et al. Locality-sensitive Hashing scheme based on p-stable distributions[C]∥Proceedings of the Annual Symposium on Computational Geometry, New York, USA, 2004: 253-262.
9	Weiss Y, Torralba A, Fergus R. Spectral Hashing[C]∥Proceedings of the Advances in Neural Information Processing Systems, British Columbia, Canada, 2008: 1753-1760.
10	Gong Y C, Svetlana L. Iterative quantization: a procrustean approach to learning binary codes[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CO, USA, 2011: 817-824.
11	He Kai-ming, Wen Fang, Sun Jian. K-means Hashing: an affinity-preserving quantization method for learning binary compact codes[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), OR, USA, 2013: 2938-2945.
12	Cao Yue, Long Ming-sheng, Liu Bin, et al. Deep Cauchy Hashing for Hamming space retrieval[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), UT, USA, 2018: 1229-1237.
13	Shen Yu-min, Qin Jie, Chen Jia-xin, et al. Auto-encoding twin-bottleneck Hashing[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), WA, USA, 2020: 2815-2824.
14	Li Peng, Ren Peng. Partial randomness Hashing for large-scale remote sensing image retrieval[J]. IEEE Geoscience & Remote Sensing Letters, 2017, 14(3): 464-468.
15	He Jun-feng, Liu Wei, Chang Shih-fu. Scalable similarity search with optimized kernel Hashing[C]∥Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, 2010: 1129-1138.
16	Liu Hao-miao, Wang Rui-ping, Shan Shi-guang, et al. Deep supervised Hashing for fast image retrieval[J]. IEEE Conference on Computer Vision and Pattern Recognition, 2016, 127(9): 2064-2072.
17	Li Yan-sheng, Zhang Yong-jun, Huang Xin, et al. Large-scale remote sensing image retrieval by deep Hashing neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(2): 950-965.
18	Fan Li-li, Zhao Hong-wei, Zhao Hao-yu. Distribution consistency loss for large-scale remote sensing image retrieval[J]. Remote Sensing, 2020, 12(1): No. 175.
19	Kamran G, Zheng F, Najmeh S, et al. Unsupervised deep generative adversarial Hashing network[C]∥Proceedings of the Conference on Computer Vision and Pattern Recognition, UT, USA, 2018: 3664-3673.
20	Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[C]∥Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico, 2016: No.06434.
21	Cao Yue, Liu Bin, Long Ming-sheng, et al. HashGAN: Deep learning to hash with pair conditional wasserstein GAN[C]∥Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 2018:1287-1296.
22	Martín A, Soumith C, Léon B. Wasserstein generative adversarial networks[C]∥Proceedings of the International Conference on Machine Learning 2017, Sydney, NSW, Australia, 2017: 214-223.
23	Augustus O, Christopher O, Jonathon S. Conditional image synthesis with auxiliary classifier GANs[C]∥Proceedings of the International Conference on Machine Learning, Sydney, NSW, Australia, 2017: 4043-4055.
24	Alex K, Ilya S, Geoffrey E H. Image-Net classification with deep convolutional neural networks[C]∥Proceedings of the NIPS, Nevada, United States, 2012: 1106-1114.
25	Yang Y, Shawn D N. Bag-of-visual-words and spatial extensions for land-use classification[C]∥Proceedings of the International Conference on Advances in Geographic Information Systems, Association for Computing Machinery, San Jose, California, 2010:270-279.
26	Saikat B, Sangram G, Supratik M, et al. DeepSat: a learning framework for satellite imagery[C]∥Proceedings of the International Conference on Advances in Geographic Information Systems, Association for Computing Machinery, Seattle, Washington, 2015: 1-10.

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长度/bit	GANOCH	TBH	DCH	PRH	KMH	ITQ	SH	LSH
64	0.5934	0.5768	0.4862	0.4361	0.3946	0.3657	0.3482	0.3407
128	0.6375	0.6124	0.4986	0.4528	0.4173	0.3856	0.3724	0.3615
256	0.6517	0.6345	0.5128	0.4857	0.4361	0.4285	0.4152	0.3986

长度/bit	GANOCH	TBH	DCH	PRH	KMH	ITQ	SH	LSH
64	0.6042	0.5826	0.4936	0.4586	0.4125	0.3764	0.3695	0.3628
128	0.6439	0.6268	0.5174	0.4795	0.4281	0.3927	0.3864	0.3752
256	0.6815	0.6527	0.5394	0.4972	0.4516	0.4359	0.4238	0.4175

长度/bit	GANOCH	TBH	DCH	PRH	KMH	ITQ	SH	LSH
64	0.3627	0.3415	0.2917	0.2462	0.2135	0.1986	0.1724	0.1637
128	0.4852	0.4638	0.3963	0.3527	0.2816	0.2462	0.2015	0.1842
256	0.5394	0.4975	0.4319	0.3746	0.3168	0.2673	0.2351	0.2148