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Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models Cover

Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models

Fibres & Textiles in Eastern Europe
Volume 30 (2022): Issue 5 (October 2022)
By: Jun Xu,  Yumeng Wei,  Aichun Wang,  Heng Zhao and  Damien Lefloch  
Open Access
|Dec 2022

Figures & Tables

Fig. 1

Research object and methodology
Research object and methodology

Fig. 2

Flow chart of clothing image classification
Flow chart of clothing image classification

Fig. 3

Schematic diagram of HOG+SVM algorithm
Schematic diagram of HOG+SVM algorithm

Fig. 4

Architecture of a simple NN
Architecture of a simple NN

Fig. 5

Architecture of the CNN
Architecture of the CNN

Fig. 6

Architecture of small VGG
Architecture of small VGG

Fig. 7

Experimental flow chart
Experimental flow chart

Fig. 8

Examples and categories of the Fashion-MNIST dataset
Examples and categories of the Fashion-MNIST dataset

Fig. 9

Accuracy comparison of HOG+SVM, NN, and CNN.
Accuracy comparison of HOG+SVM, NN, and CNN.

Fig. 10

Example images of the Fashion144k dataset
Example images of the Fashion144k dataset

Fig. 11

Example images of the SmallV1 dataset
Example images of the SmallV1 dataset

Fig. 12

Recognition accuracy of HOG+SVM and Small VGG for the SmallV1 dataset
Recognition accuracy of HOG+SVM and Small VGG for the SmallV1 dataset

Fig. 13

Recognition accuracy of Small VGG network models in different datasets
Recognition accuracy of Small VGG network models in different datasets

Fig. 14

Recognition accuracy of the GhostNet model for different datasets
Recognition accuracy of the GhostNet model for different datasets

Fig. 15

Accuracy of Small VGG and GhostNet for different numbers of datasets
Accuracy of Small VGG and GhostNet for different numbers of datasets

Fashion-MNIST dataset label description

DescriptionT-shirt/topTrouserPulloverDressNotes
Label01234
DescriptionSandalShirtSneakerBagAnkle boot
Label56789

Highest accuracy comparison of different models

NameHighest Accuracy
ML (rbf_4x4)91.3%
CNN89.7%
ML (Linear_4x4)88.9%
NN87.7%
ML (rbf_8x8)86.7%
ML (Linear_8x8)83.1%
DOI: https://doi.org/10.2478/ftee-2022-0046 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
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Language: English
Page range: 66 - 78
Published on: Dec 22, 2022
Published by: Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
E-commerce,
clothing image classification,
traditional machine learning,
CNN,
HOG+SVM,
small VGG network
Related subjects:
Business and economics,
Business management,
Business informatics,
Process management,
Industrial chemistry,
Cellulose, paper and textiles,
Polymer science and technology,
Materials sciences,
Biomaterials and natural materials

© 2022 Jun Xu, Yumeng Wei, Aichun Wang, Heng Zhao, Damien Lefloch, published by Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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