High Precision Self-learning Hashing for Image Re

High Precision Self-learning Hashing for Image

Retrieval

Jia-run Fu1,Ling-yu Yan1(&),Lu Yuan1,Yan Zhou2,

Hong-xin Zhang1,and Chun-zhi Wang1

1School of Computer Science,Hubei University of Technology,

Wuhan430068,Hubei,China

lingboli1997@https://www.360docs.net/doc/532541091.html,

2Hubei Entry-Exit Inspection and Quarantine Bureau,

Wuhan430050,Hubei,China

Abstract.At present,hashing algorithm has been combined with deep learning

to accelerate image retrieval.Against this background,there are many ways to

construct hashing,but most of the methods do not show excellent performance

in reducing semantic loss.At the same time,the vast majority of cases that adopt

hashing algorithm and obtain successful cases involve the identi?cation model

requiring labels.So we propose a high precision with the combination of self-

learning hash algorithm(HPSLH)to conduct experiments,the algorithm can not

only through the analysis of the data itself,and construct a set of false label,then

using the data from the identi?cation model of deep learning can also avoid

enormous semantic loss in the process of our hash.Through experiments on

traditional datasets,this method can achieve the desired goal.

Keywords:Self-learningáDeep learningáHashingáImage retrieval

1Introduction

With the advent of the era of big data,data volume compared with the previous grew exponentially,extracted from a mass of complex image data we want to be associated with similar image data,is the core of this background attention.In this context,the content based image hashing ef?cient image retrieval has attracted the attention of researchers.Hashing maps high-dimensional features to the compact hash code,and then computes the hamming distance between two binary hash codes via a PC.For example,use the XOR operation to complete the operation.In the?eld of fast similarity search,hashing algorithm shows great advantages.

Based on this,more and more people began to use hashing to solve problems,and put their eyes on the direction of further improving operation speed and improving accuracy.For now,most of the hash algorithms are based on manual label,but manual label is quite limited.In order to solve the above problems,deep learning is introduced into the study of hashing algorithm.The?rst hash algorithm based on deep learning is the Semantic Hashing method proposed by Hinton research group[1].After that,the combination of deep learning and hash is widely studied.The emergence of CNNH[2] opened a new chapter in the study.In the experiment,CNNH has achieved remarkable ?Springer Nature Singapore Pte Ltd.2018

Q.Zhou et al.(Eds.):ICPCSEE2018,CCIS901,pp.689–697,2018.

https://https://www.360docs.net/doc/532541091.html,/10.1007/978-981-13-2203-7_57