Genetic approach based image retrieval by using CCM and textual features

P. Shrivas, U. Lilhore, Nitin Agrawal
{"title":"Genetic approach based image retrieval by using CCM and textual features","authors":"P. Shrivas, U. Lilhore, Nitin Agrawal","doi":"10.1109/ICRITO.2017.8342453","DOIUrl":null,"url":null,"abstract":"As the quantity of web clients are expanding every day. This work concentrate on the retrieval of pictures by using the visual and annotation characteristics of the images. In this work two kind of features are utilized for the bunching of the picture dataset. So Based on the comparability of content and CCM components of the picture bunches are made. For bunching here genetic approach is utilized. Two phase learning genetic algorithm named as teacher learning based optimization was utilized for clustring. Here client pass two kind of queries first was content while other is image, this assistance in choosing suitable cluster for retrieval of picture. Analysis was done on genuine and artificial set of pictures. Result demonstrates that proposed work is better on various assessment parameters as contrast with existing strategies.","PeriodicalId":357118,"journal":{"name":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","volume":"8 4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRITO.2017.8342453","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

As the quantity of web clients are expanding every day. This work concentrate on the retrieval of pictures by using the visual and annotation characteristics of the images. In this work two kind of features are utilized for the bunching of the picture dataset. So Based on the comparability of content and CCM components of the picture bunches are made. For bunching here genetic approach is utilized. Two phase learning genetic algorithm named as teacher learning based optimization was utilized for clustring. Here client pass two kind of queries first was content while other is image, this assistance in choosing suitable cluster for retrieval of picture. Analysis was done on genuine and artificial set of pictures. Result demonstrates that proposed work is better on various assessment parameters as contrast with existing strategies.
基于CCM和文本特征的遗传图像检索方法
随着网络客户端的数量每天都在扩大。这项工作主要是利用图像的视觉特征和注释特征来检索图像。在这项工作中,两种特征被用于图像数据集的聚类。因此,基于内容和CCM的可比性,对图片进行了分组。这里的聚束采用遗传方法。采用基于教师学习的两阶段学习遗传算法对聚类进行优化。客户端通过两种查询,一种是内容查询,另一种是图像查询,这有助于选择合适的聚类进行图像检索。对真品和仿品进行了分析。结果表明,与现有的评估策略相比,本文所提出的工作在各评估参数上都有较好的效果。
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