Image Retrieval Based on Bit-Plane Distribution Entropy

Z. Shan, Wang Hai-tao
{"title":"Image Retrieval Based on Bit-Plane Distribution Entropy","authors":"Z. Shan, Wang Hai-tao","doi":"10.1109/CSSE.2008.270","DOIUrl":null,"url":null,"abstract":"Based on the analysis of color histogram for image retrieval, a new descriptor, bit-plane distribution entropy (BPDE), is proposed in this paper. The image is firstly divided into eight bit-planes and the Gray-code of bit-planes is introduced to avoid the effect of changes in the intensity values on bit-planes. Then, an entropy vector is constructed by computing the entropy of the first four significant planes which contain most of the structural information of the image. Finally, with designing of the correlation-weighted matrix, the Mahalanobis distance is adopted to measure the similarity because of the correlation between the concerned vectors. Comparisons are conducted between BPDE and other descriptors. Experimental results show that the proposed method provides more significantly retrieval results than the traditional ones.","PeriodicalId":6460,"journal":{"name":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","volume":"187 1","pages":"532-535"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSSE.2008.270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

Abstract

Based on the analysis of color histogram for image retrieval, a new descriptor, bit-plane distribution entropy (BPDE), is proposed in this paper. The image is firstly divided into eight bit-planes and the Gray-code of bit-planes is introduced to avoid the effect of changes in the intensity values on bit-planes. Then, an entropy vector is constructed by computing the entropy of the first four significant planes which contain most of the structural information of the image. Finally, with designing of the correlation-weighted matrix, the Mahalanobis distance is adopted to measure the similarity because of the correlation between the concerned vectors. Comparisons are conducted between BPDE and other descriptors. Experimental results show that the proposed method provides more significantly retrieval results than the traditional ones.
基于位面分布熵的图像检索
在分析颜色直方图用于图像检索的基础上,提出了一种新的描述符——位平面分布熵(BPDE)。首先将图像划分为8个位平面,并引入位平面的灰度编码,避免了强度值变化对位平面的影响;然后,通过计算包含图像大部分结构信息的前四个有效平面的熵来构造熵向量;最后,通过相关加权矩阵的设计,由于相关向量之间的相关性,采用马氏距离来度量相似度。对BPDE和其他描述符进行了比较。实验结果表明,该方法比传统方法的检索效果更显著。
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