Research on Clothing Image Classification by Convolutional Neural Networks

被引：0

作者：

Chen, Lili ^{[1
]}

Han, Runping ^{[2
]}

Xing, Shaopeng ^{[3
]}

Ru, Shuiqiang ^{[3
]}

机构：

[1] Beijing Inst Fash Technol, Sch Informat Engn, Beijing 100029, Peoples R China

[2] Beijing Inst Fash Technol, Chinese Fash & Technol Res Inst, Beijing 100029, Peoples R China

[3] Beijing Dahao Technol Corp Ltd, Beijing 100029, Peoples R China

来源：

2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018) | 2018年

关键词：

component; Convolutional neural network; Inception module; Residual block; Transfer learning; Pre-trained Inception-v3;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

In recent years, clothing image classification methods based on convolutional neural networks(CNNs) have attracted plenty of attention with the increasing demand for high accuracy clothing image classification in many fields. In this paper, five different CNNs are designed to implement clothing image classification, which are the conventional CNN, the CNN containing inception module, the CNN containing inception module and residual block, two transfer learned CNNs. The experimental results show that all the networks are capable of achieving good classification, among which the transfer learned CNN have higher classification accuracy.

引用

页数：5

共 50 条

[1] Clothing Classification Using Convolutional Neural Networks
Hodecker, Andrei
Fernandes, Anita M. R.
Steffens, Alisson
Crocker, Paul
Leithardt, Valderi R. Q.
[J]. 2020 15TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2020), 2020,
[2] Convolutional Neural Networks for image classification
Jmour, Nadia
Zayen, Sehla
Abdelkrim, Afef
[J]. 2018 INTERNATIONAL CONFERENCE ON ADVANCED SYSTEMS AND ELECTRICAL TECHNOLOGIES (IC_ASET), 2017, : 397 - 402
[3] Research on Image Classification Based on HP - Net Convolutional Neural Networks
Wang, Qiang
Li, Xiaojie
Shi, Canghong
[J]. PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2017, : 1917 - 1922
[4] CONVOLUTIONAL NEURAL NETWORKS IN THE TASK OF IMAGE CLASSIFICATION
Zelenina, Larisa
Khaimina, Liudmila
Khaimin, Evgenii
Khripunov, D.
Zashikhina, Inga
[J]. MATHEMATICS AND INFORMATICS, 2022, 65 (01): : 19 - 29
[5] Convolutional neural networks for hyperspectral image classification
Yu, Shiqi
Jia, Sen
Xu, Chunyan
[J]. NEUROCOMPUTING, 2017, 219 : 88 - 98
[6] Convolutional Neural Networks for Document Image Classification
Kang, Le
Kumar, Jayant
Ye, Peng
Li, Yi
Doermann, David
[J]. 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2014, : 3168 - 3172
[7] Hyperspectral Image Classification with Convolutional Neural Networks
Slavkovikj, Viktor
Verstockt, Steven
De Neve, Wesley
Van Hoecke, Sofie
Van de Walle, Rik
[J]. MM'15: PROCEEDINGS OF THE 2015 ACM MULTIMEDIA CONFERENCE, 2015, : 1159 - 1162
[8] Preprocessing for Image Classification by Convolutional Neural Networks
Pal, Kuntal Kumar
Sudeep, K. S.
[J]. 2016 IEEE INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ELECTRONICS, INFORMATION & COMMUNICATION TECHNOLOGY (RTEICT), 2016, : 1778 - 1781
[9] Deformable Convolutional Neural Networks for Hyperspectral Image Classification
Zhu, Jian
Fang, Leyuan
Ghamisi, Pedram
[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (08) : 1254 - 1258
[10] Hyperspectral Image Classification using Convolutional Neural Networks
Shambulinga, M.
Sadashivappa, G.
[J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (06) : 702 - 708

← 1 2 3 4 5 →