<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Clothing image in the e-commerce industry plays an important role in providing customers with information. This paper divides clothing images into two groups: pure clothing images and dressed clothing images. Targeting small and medium-sized clothing companies or merchants, it compares traditional machine learning and deep learning models to determine suitable models for each group. For pure clothing images, the HOG+SVM algorithm with the Gaussian kernel function obtains the highest classification accuracy of 91.32% as compared to the Small VGG network. For dressed clothing images, the CNN model obtains a higher accuracy than the HOG+SVM algorithm, with the highest accuracy rate of 69.78% for the Small VGG network. Therefore, for end-users with only ordinary computing processors, it is recommended to apply the traditional machine learning algorithm HOG+SVM to classify pure clothing images. The classification of dressed clothing images is performed using a more efficient and less computationally intensive lightweight model, such as the Small VGG network.</p>
</abstract>

Clothing image in the e-commerce industry plays an important role in providing customers with information. This paper divides clothing images into two groups: pure clothing images and dressed clothing images. Targeting small and medium-sized clothing companies or merchants, it compares traditional machine learning and deep learning models to determine suitable models for each group. For pure clothing images, the HOG+SVM algorithm with the Gaussian kernel function obtains the highest classification accuracy of 91.32% as compared to the Small VGG network. For dressed clothing images, the CNN model obtains a higher accuracy than the HOG+SVM algorithm, with the highest accuracy rate of 69.78% for the Small VGG network. Therefore, for end-users with only ordinary computing processors, it is recommended to apply the traditional machine learning algorithm HOG+SVM to classify pure clothing images. The classification of dressed clothing images is performed using a more efficient and less computationally intensive lightweight model, such as the Small VGG network.

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

Fibres & Textiles in Eastern Europe

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Clothing image in the e-commerce industry plays an important role in providing customers with information. This paper divides clothing images into two groups:...

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

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%

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

Article Category: Research Article

Online veröffentlicht: 22. Dez. 2022

Seitenbereich: 66 - 78

DOI: https://doi.org/10.2478/ftee-2022-0046

Schlüsselwörter
E-commerce, clothing image classification, traditional machine learning, CNN, HOG+SVM, small VGG network

© 2022 Jun Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Fashion-MNIST dataset label description

Highest accuracy comparison of different models

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

Article Category: Research Article

Online veröffentlicht: 22. Dez. 2022

Seitenbereich: 66 - 78

DOI: https://doi.org/10.2478/ftee-2022-0046

SchlüsselwörterE-commerce, clothing image classification, traditional machine learning, CNN, HOG+SVM, small VGG network

© 2022 Jun Xu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Fashion-MNIST dataset label description

Highest accuracy comparison of different models

Schlüsselwörter
E-commerce, clothing image classification, traditional machine learning, CNN, HOG+SVM, small VGG network