An Image Retrieval Method Based on r/KPSO

Xu Zhang, B. Guo, Guiyue Zhang, Yunyi Yan
{"title":"An Image Retrieval Method Based on r/KPSO","authors":"Xu Zhang, B. Guo, Guiyue Zhang, Yunyi Yan","doi":"10.1109/IBICA.2011.22","DOIUrl":null,"url":null,"abstract":"Image retrieval is a hot and hard technology in the field of computing science. In this paper, a method named r/KPSO (Particle Swarm Optimization with r- and K-selection) is applied in relevance feedback (RF) of image retrieval. The main idea of r/KPSO is inspired by the r- and K-selection of Ecology. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. Based on r/KPSO, we define the positive and negative feedback samples as study principle, and optimize weightings according to user's retrieval requirement. Experiments show that both the recall and precision are improved effectively.","PeriodicalId":158080,"journal":{"name":"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Second International Conference on Innovations in Bio-inspired Computing and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IBICA.2011.22","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

Image retrieval is a hot and hard technology in the field of computing science. In this paper, a method named r/KPSO (Particle Swarm Optimization with r- and K-selection) is applied in relevance feedback (RF) of image retrieval. The main idea of r/KPSO is inspired by the r- and K-selection of Ecology. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. Based on r/KPSO, we define the positive and negative feedback samples as study principle, and optimize weightings according to user's retrieval requirement. Experiments show that both the recall and precision are improved effectively.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于r/KPSO的图像检索方法
图像检索是计算机科学领域的一个热点和难点技术。本文将r/KPSO (Particle Swarm Optimization with r- and K-selection)方法应用于图像检索的相关反馈。r/KPSO的主要思想受到生态学r-和k -选择的启发。r-选择表现为数量多、亲本照顾少、生长率大、发育快;k -选择表现为质量多、亲本照顾多、生长率小、发育慢。在r/KPSO的基础上,定义正负反馈样本作为研究原则,并根据用户的检索需求优化权重。实验表明,该方法有效地提高了查全率和查准率。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fighting Detection Based on Optical Flow Context Histogram Some Researches for Lorenz-Based Secure Communication in Time and Frequency Domains A Cognitive Model to Mimic an Aspect of Low Level Perception of Sound: Modelling Reverberation Perception by Statistical Signal Analysis The Sustained Exhilarating Cardiac Responses after Listening to the Very Fast and Complex Rhythm Smart Classroom Roll Caller System with IOT Architecture
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1