基于稀疏自编码的无监督哈希算法

张丽萍; 孟卫平; 谭家海

doi:10.3788/YJYXS20183311.0950

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基于稀疏自编码的无监督哈希算法

图像处理 | 更新时间：2020-08-12

- 基于稀疏自编码的无监督哈希算法
- Unsupervised Hashing algorithm based on sparse autoencoder
- 液晶与显示 2018年33卷第11期页码：950-957
- 作者机构：
  
  1.陕西国际商贸学院, 陕西西安 712046
  2.中国科学院西安光学精密机械研究所, 陕西西安 710119
- 作者简介：
  
  [ "张丽萍(1975-), 女, 山东威海人, 副教授, 毕业于长安大学地测学院土地资源管理专业, 研究方向地理信息系统。E-mail:zhangliping@163.com" ]
  [ "谭家海(1983-), 男, 陕西石泉人, 毕业于西安电子科技大学软件工程专业, 研究方向软件设计、图像处理及信息系统处理。E-mail: tanjiahai@opt.cn" ]
- 基金信息：
  
  陕西省科技厅农业科技公关项目(2015NY061);Shaanxi Science and Technology Department Agricultural Science and Technology Public Relations Project"Research on Shaanxi Dynamic Agricultural Products Information System Based on 3S"(2015NY061)
- DOI：10.3788/YJYXS20183311.0950
  中图分类号： O431.2;O431.1
- 收稿日期：2018-06-20，
  
  录用日期：2018-7-9，
  
  纸质出版日期：2018-11-05
- 稿件说明：
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张丽萍, 孟卫平, 谭家海. 基于稀疏自编码的无监督哈希算法[J]. 液晶与显示, 2018,33(11):950-957.

Li-ping ZHANG, Wei-ping MENG, Jia-hai TAN. Unsupervised Hashing algorithm based on sparse autoencoder[J]. Chinese journal of liquid crystals and displays, 2018, 33(11): 950-957.
张丽萍, 孟卫平, 谭家海. 基于稀疏自编码的无监督哈希算法[J]. 液晶与显示, 2018,33(11):950-957. DOI： 10.3788/YJYXS20183311.0950.

Li-ping ZHANG, Wei-ping MENG, Jia-hai TAN. Unsupervised Hashing algorithm based on sparse autoencoder[J]. Chinese journal of liquid crystals and displays, 2018, 33(11): 950-957. DOI： 10.3788/YJYXS20183311.0950.

摘要

最近邻搜索在大规模图像检索中变得越来越重要。在最近邻搜索中，许多哈希方法因为快速查询和低内存被提出。然而，现有方法在哈希函数构造过程中对数据稀疏结构研究的不足，本文提出了一种无监督的稀疏自编码的图像哈希方法。基于稀疏自编码的图像哈希方法将稀疏构造过程引入哈希函数的学习过程中，即通过利用稀疏自编码器的KL距离对哈希码进行稀疏约束以增强局部保持映射过程中的判别性，同时利用L2范数来哈希编码的量化误差。实验中用两个公共图像检索数据集CIFAR-10和YouTube Faces验证了本文算法相比其他无监督哈希算法的优越性。

Abstract

Nearest neighbor search is becoming more and more important in large scale image retrieval.Many hash methods are proposed in nearest neighbor search owing to fast query and low memory. However

there is a lack of research on the sparse structure of data in the process of hash function construction. The proposed hashing method introduces the sparse construction process into the learning process of hash function and uses KL distance of sparse autoencoder on hash code sparse constraints to enhance locality preserving discriminant mapping process. The proposed method leverages L2 norm to control quantization error in hash encoding. The experimental results on two common image retrieval datasets CIFAR-10 and YouTube Faces show that the proposed algorithm is superior to other unsupervised hashing algorithms.

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references

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