Hybrid color model for image retrieval based on fuzzy histograms

Vedran Ljubovic, H. Supic
{"title":"Hybrid color model for image retrieval based on fuzzy histograms","authors":"Vedran Ljubovic, H. Supic","doi":"10.1145/2643188.2643198","DOIUrl":null,"url":null,"abstract":"A hybrid color model is a color descriptor formed by combining different channels from several other color models. In computer graphics applications such models are rarely used due to redundancy. However, hybrid color models may be of interest for Content-Based Image Retrieval (CBIR). Best features of each color model can be combined to obtain optimum retrieval performance. In this paper, a novel algorithm is proposed for selection of channels for a hybrid color model used in construction of a fuzzy color histogram. This algorithm is elaborated and implemented for use with several common reference datasets consisting of photographs of natural scenes. Result of this experimental procedure is a new hybrid color model named HSY. Using standard datasets and a standard metric for retrieval performance (ANMRR), it is shown that this new model can give an improved retrieval performance. In addition, this model is of interest for use in JPEG compressed domain due to simpler calculation.","PeriodicalId":115384,"journal":{"name":"Proceedings of the 30th Spring Conference on Computer Graphics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 30th Spring Conference on Computer Graphics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2643188.2643198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

A hybrid color model is a color descriptor formed by combining different channels from several other color models. In computer graphics applications such models are rarely used due to redundancy. However, hybrid color models may be of interest for Content-Based Image Retrieval (CBIR). Best features of each color model can be combined to obtain optimum retrieval performance. In this paper, a novel algorithm is proposed for selection of channels for a hybrid color model used in construction of a fuzzy color histogram. This algorithm is elaborated and implemented for use with several common reference datasets consisting of photographs of natural scenes. Result of this experimental procedure is a new hybrid color model named HSY. Using standard datasets and a standard metric for retrieval performance (ANMRR), it is shown that this new model can give an improved retrieval performance. In addition, this model is of interest for use in JPEG compressed domain due to simpler calculation.
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
基于模糊直方图的图像检索混合颜色模型
混合颜色模型是通过组合来自多个其他颜色模型的不同通道而形成的颜色描述符。在计算机图形学应用中,由于冗余,这种模型很少使用。然而,混合颜色模型可能是基于内容的图像检索(CBIR)的兴趣。每个颜色模型的最佳特征可以组合在一起,以获得最佳的检索性能。本文提出了一种用于构建模糊颜色直方图的混合颜色模型的通道选择算法。本文详细阐述了该算法,并将其应用于几种常见的由自然场景照片组成的参考数据集。这一实验过程的结果是一个新的混合颜色模型HSY。使用标准数据集和标准检索性能度量(ANMRR),表明该模型可以提高检索性能。此外,由于计算简单,该模型也适用于JPEG压缩领域。
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
Fast and Furious: How the web got turbo charged just in time… Fast and Furious: How the web got turbo charged just in time? Proceedings of the 30th Spring Conference on Computer Graphics Modeling and representing materials in the wild Kinect-supported dataset creation for human pose estimation
×
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