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

Jun Xu

Jun Xu

Tiangong UniversityTianjin, China
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Xu, Jun
, 
Yumeng Wei

Yumeng Wei

Tiangong UniversityTianjin, China
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Wei, Yumeng
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Aichun Wang

Aichun Wang

Tiangong UniversityTianjin, China
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Wang, Aichun
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Heng Zhao

Heng Zhao

Shenzhen Technology UniversityShenzhen, China
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Zhao, Heng
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Damien Lefloch

Damien Lefloch

Tiangong UniversityTianjin, China
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Lefloch, Damien
  
22 déc. 2022
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Catégorie d'article: Research Article
Publié en ligne: 22 déc. 2022
Pages: 66 - 78
DOI: https://doi.org/10.2478/ftee-2022-0046
Mots clés
© 2022 Jun Xu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Description T-shirt/top Trouser Pullover Dress Notes
Label 0 1 2 3 4
Description Sandal Shirt Sneaker Bag Ankle boot
Label 5 6 7 8 9

Highest accuracy comparison of different models

Name Highest Accuracy
ML (rbf_4x4) 91.3%
CNN 89.7%
ML (Linear_4x4) 88.9%
NN 87.7%
ML (rbf_8x8) 86.7%
ML (Linear_8x8) 83.1%
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Anglais
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Volume Open
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