Machine learning framework for image classification

Sehla Loussaief, A. Abdelkrim
{"title":"Machine learning framework for image classification","authors":"Sehla Loussaief, A. Abdelkrim","doi":"10.1109/DT.2017.8012075","DOIUrl":null,"url":null,"abstract":"Hereby in this paper, we are interested to extraction methods and classification in case of image classification and recognition application. We expose the performance of training models on varying classifier algorithms on Caltech 101 images categories. For feature extraction functions we evaluate the use of the classical SURF technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images. The trained model will be integrated into a robotic system in a future work.","PeriodicalId":426951,"journal":{"name":"2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DT.2017.8012075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Hereby in this paper, we are interested to extraction methods and classification in case of image classification and recognition application. We expose the performance of training models on varying classifier algorithms on Caltech 101 images categories. For feature extraction functions we evaluate the use of the classical SURF technique against global color feature extraction. The purpose of our work is to guess the best machine learning framework techniques to recognize the stop sign images. The trained model will be integrated into a robotic system in a future work.
图像分类的机器学习框架
在本文中,我们对图像分类和识别应用中的提取方法和分类感兴趣。我们在Caltech 101图像类别上展示了不同分类器算法上训练模型的性能。对于特征提取函数,我们评估了经典SURF技术与全局颜色特征提取的使用。我们工作的目的是猜测识别停车标志图像的最佳机器学习框架技术。经过训练的模型将在未来的工作中集成到机器人系统中。
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