Explicit foreground and background modeling in the classification of text blocks in scene images

B. Sriman, Lambert Schomaker
{"title":"Explicit foreground and background modeling in the classification of text blocks in scene images","authors":"B. Sriman, Lambert Schomaker","doi":"10.1109/ACPR.2015.7486604","DOIUrl":null,"url":null,"abstract":"Achieving high accuracy for classifying foreground and background is an interesting challenge in the field of scene image analysis because of the wide range of illumination, complex background, and scale changes. Classifying foreground and background using bag-of-feature model gives a good result. However, the performance of the classifier depends on designed features. Therefore, this paper presents an alternative classification method based on three categories of object-attributes features namely object description, color distribution and gradient strength. Each feature is computed to a classifier model. The robustness of the method has been tested on the ICDAR2015 dataset. The experimental results show that the performance of the proposed method performs competitively against the results of existing methods in term of precision and recall.","PeriodicalId":240902,"journal":{"name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACPR.2015.7486604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Achieving high accuracy for classifying foreground and background is an interesting challenge in the field of scene image analysis because of the wide range of illumination, complex background, and scale changes. Classifying foreground and background using bag-of-feature model gives a good result. However, the performance of the classifier depends on designed features. Therefore, this paper presents an alternative classification method based on three categories of object-attributes features namely object description, color distribution and gradient strength. Each feature is computed to a classifier model. The robustness of the method has been tested on the ICDAR2015 dataset. The experimental results show that the performance of the proposed method performs competitively against the results of existing methods in term of precision and recall.
场景图像文本块分类中明确的前景和背景建模
由于场景图像的光照范围广、背景复杂、尺度变化大,实现前景和背景的高精度分类是场景图像分析领域的一个有趣的挑战。利用特征袋模型对前景和背景进行分类,取得了较好的效果。然而,分类器的性能取决于设计的特征。因此,本文提出了一种基于物体描述、颜色分布和梯度强度这三类物体属性特征的分类方法。每个特征被计算为一个分类器模型。该方法的鲁棒性已在ICDAR2015数据集上进行了测试。实验结果表明,该方法在查准率和查全率方面与现有方法具有相当的竞争力。
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:604180095
Book学术官方微信