An Improved Binary Hash Coding Method

JIANG Ze-tao; JIAN Xiong; LIU Xiao-yan; ZENG Cong-wen

doi:10.3969/j.issn.0372-2112.2019.02.029

您当前的位置：

首页 >

文章列表页 >

An Improved Binary Hash Coding Method

更新时间：2025-07-16

- An Improved Binary Hash Coding Method
- Acta Electronica Sinica Vol. 47, Issue 2, Pages: 462-469(2019)
- 作者机构：
  
  1. 桂林电子科技大学广西高校图像图形智能处理重点实验室,广西,桂林,541004
  2. 桂林电子科技大学广西可信软件重点实验室,广西,桂林,541004
  3. 桂林电子科技大学广西高校图像图形智能处理重点实验室,广西,桂林,541004
  4. 桂林电子科技大学广西可信软件重点实验室,广西,桂林,541004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.02.029
  CLC： TP391.41
- Published Online：25 February 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
JIANG Ze-tao, JIAN Xiong, LIU Xiao-yan, et al. An Improved Binary Hash Coding Method[J]. Acta Electronica Sinica, 2019, 47(2): 462-469.
DOI：

JIANG Ze-tao, JIAN Xiong, LIU Xiao-yan, et al. An Improved Binary Hash Coding Method[J]. Acta Electronica Sinica, 2019, 47(2): 462-469. DOI： 10.3969/j.issn.0372-2112.2019.02.029.

摘要

为了应对手工视觉特征与哈希编码过程不能最佳地兼容以及现有哈希方法无法区分图像语义信息的问题，提出一种基于深度卷积神经网络学习二进制哈希编码的方法.该方法基本思想是在深度残差网络中增加一个哈希层，同时学习图像特征和哈希函数；以此同时提出一种更加紧凑的分级哈希结构，用来提取更加接近图像语义的特征.经MNIST、CIFAR-10、NUS-WIDE数据集的实验，结果表明该方法优于现有的哈希方法.该方法不仅统一了特征学习和哈希编码的过程，同时深层残差网络也能得到更接近图像语义的特征，进而提高了检索准确度.

Abstract

To address the problems that hand-engineering visual features can't be optimally compatible with the Hash coding process and existing Hash methods can't differentiate images semantics information

a learning method of binary Hashing based on deep convolutional neural networks is proposed.The basic idea is to add a Hash layer into the deep residual network and to learn simultaneously image features and Hash functions.Meanwhile

we propose a more compact hierarchical Hashing structure to extract features closer to semantics information of images.Experimental results of MNIST

CIFAR-10 and NUS-WIDE datasets show that the method is superior to existing Hashing methods.This method not only unifies the process of feature learning and Hash coding

and at the same time

the deep residual network is able to get features closer to image semantics.Thus the retrieval accuracy is improved.

关键词

Keywords

references

Views

383

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

An Image Retrieval Method Synthesizing PHOG Shape Description and Wavelet Pyramid Energy Distribution

Shape Description Using Arc-Height Radius Complex Function

Image Retrieval Relevance Feedback Algorithm Based on Particle Swarm Optimization

Related Author

WANG Li-dong

WEI Bao-gang

YUAN Jie

WANG Bin

FONT

DING Xing-

LIAO Ying-hao

LIU Pei-zhong

Related Institution

Hangzhou Normal UniversityHongzhouZhejiang 310012China

College of Computer Science and TechnologyZhejiang UniversityHangzhouZhejiang 310027China

Hangzhou Normal University,Hongzhou

College of Computer Science and Technology, Zhejiang University

School of biomedical engineeringSoutheast UniversityNanjingJiangsu 210096China

⁰