Convolutional Neural Networks for image classification

被引：0

作者：

Jmour, Nadia ^{[1
]}

Zayen, Sehla ^{[1
]}

Abdelkrim, Afef ^{[1
]}

机构：

[1] Tunis Natl Engn Sch Carthage, Natl Engn Sch, LARA Lab, Tunis, Tunisia

来源：

2018 INTERNATIONAL CONFERENCE ON ADVANCED SYSTEMS AND ELECTRICAL TECHNOLOGIES (IC_ASET) | 2017年

关键词：

Convolutional neural network; Deep Learning; Transfer Learning; ImageNet;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper describes a learning approach based on training convolutional neural networks (CNN) for a traffic sign classification system. In addition, it presents the preliminary classification results of applying this CNN to learn features and classify RGB-D images task. To determine the appropriate architecture, we explore the transfer learning technique called "fine tuning technique", of reusing layers trained on the ImageNet dataset in order to provide a solution for a four -class classification task of a new set of data.

引用

页码：397 / 402

页数：6

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