A Model of Visual Attention for Natural Image Retrieval

Guanghai Liu, D. Fan
{"title":"A Model of Visual Attention for Natural Image Retrieval","authors":"Guanghai Liu, D. Fan","doi":"10.1109/ISCC-C.2013.21","DOIUrl":null,"url":null,"abstract":"In this paper, saliency textons model is proposed to encode color, orientation and saliency cue and spatial information as image features for CBIR, where the image representation is so called saliency textons histogram. Experimental results indicate that the performances of saliency textons histogram outperform Gabor filter and multi-text on histogram. The saliency textons histogram can combine color feature, edge feature and spatial layout together. Furthermore, saliency textons model can simulate visual attention mechanism.","PeriodicalId":313511,"journal":{"name":"2013 International Conference on Information Science and Cloud Computing Companion","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"40","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Information Science and Cloud Computing Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC-C.2013.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 40

Abstract

In this paper, saliency textons model is proposed to encode color, orientation and saliency cue and spatial information as image features for CBIR, where the image representation is so called saliency textons histogram. Experimental results indicate that the performances of saliency textons histogram outperform Gabor filter and multi-text on histogram. The saliency textons histogram can combine color feature, edge feature and spatial layout together. Furthermore, saliency textons model can simulate visual attention mechanism.
自然图像检索中的视觉注意模型
本文提出了显著性文本模型,将颜色、方向、显著性线索和空间信息编码为CBIR的图像特征,其中图像表示称为显著性文本直方图。实验结果表明,显著性文本直方图的性能优于Gabor滤波和多文本直方图。显著性文本直方图可以将颜色特征、边缘特征和空间布局结合在一起。此外,显著性文本模型可以模拟视觉注意机制。
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