Deep learning techniques for image recognition of counterfeited luxury handbags materials

P. Apipawinwongsa, Y. Limpiyakorn
{"title":"Deep learning techniques for image recognition of counterfeited luxury handbags materials","authors":"P. Apipawinwongsa, Y. Limpiyakorn","doi":"10.1117/12.2644669","DOIUrl":null,"url":null,"abstract":"Due to the fact that counterfeit in second-handed goods terribly affects trading in markets of second-handed luxury bags, users in this research thus present studies of methods to classify genuineness of ‘Gucci GG Canvas’ with the pretrained model from Model VGG16 and with DenseNet121 to design deep Convolutional Neural Networks (CNN) model for binary classification. The CNN together with DenseNet121 model comprises accuracy at 95%, which is more than the 2 prior models, i.e., CNN from scratch and CNN together with VGG16.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"236 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Digital Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2644669","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

Due to the fact that counterfeit in second-handed goods terribly affects trading in markets of second-handed luxury bags, users in this research thus present studies of methods to classify genuineness of ‘Gucci GG Canvas’ with the pretrained model from Model VGG16 and with DenseNet121 to design deep Convolutional Neural Networks (CNN) model for binary classification. The CNN together with DenseNet121 model comprises accuracy at 95%, which is more than the 2 prior models, i.e., CNN from scratch and CNN together with VGG16.
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
基于深度学习技术的奢侈品手袋仿冒材料图像识别
由于二手假货严重影响二手奢侈品市场的交易,因此本研究的用户利用VGG16模型的预训练模型,利用DenseNet121设计深度卷积神经网络(CNN)模型进行二元分类,研究了“Gucci GG Canvas”真伪的分类方法。CNN与DenseNet121模型的准确率达到95%,高于之前的2个模型,即CNN from scratch和CNN with VGG16。
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
Ship detection in optical remote sensing images based on saliency and rotation-invariant feature Deformable voxel grids for shape comparisons Correction of images projected on non-white surfaces based on deep neural network Self-supervision based super-resolution approach for light field refocused image Multi-visual information fusion and aggregation for video action classification
×
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