A novel method for multiple-query image retrieval

M. Taghizadeh, A. Chalechale
{"title":"A novel method for multiple-query image retrieval","authors":"M. Taghizadeh, A. Chalechale","doi":"10.1109/SPIS.2015.7422313","DOIUrl":null,"url":null,"abstract":"Multiple-query image retrieval is usually utilized in order to enhance performance of the image retrieval system with considering single semantic for a query set. So far, multiple-query image retrieval based on different queries has rarely studied. In this work, we intend to address this problem using a binary component vector. This vector indicates distinct components which exist in an image. The binary component vector is also generated utilizing low-level feature extraction techniques. The final image retrieval process is performed based on this vector. The experimental results show a better performance and less computation in contrary to previous proposed methods.","PeriodicalId":424434,"journal":{"name":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Signal Processing and Intelligent Systems Conference (SPIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPIS.2015.7422313","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

Multiple-query image retrieval is usually utilized in order to enhance performance of the image retrieval system with considering single semantic for a query set. So far, multiple-query image retrieval based on different queries has rarely studied. In this work, we intend to address this problem using a binary component vector. This vector indicates distinct components which exist in an image. The binary component vector is also generated utilizing low-level feature extraction techniques. The final image retrieval process is performed based on this vector. The experimental results show a better performance and less computation in contrary to previous proposed methods.
一种新的多查询图像检索方法
为了提高图像检索系统的性能,通常采用多查询图像检索,同时考虑查询集的单一语义。到目前为止,基于不同查询的多查询图像检索的研究还很少。在这项工作中,我们打算使用二进制分量向量来解决这个问题。这个向量表示图像中存在的不同分量。利用低级特征提取技术生成二元分量向量。最终的图像检索过程是基于这个向量执行的。实验结果表明,该方法比以往提出的方法性能更好,计算量更少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信