- Tytuł:
- Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models
- Autorzy:
-
Xu, Jun
Wei, Yumeng
Wang, Aichun
Zhao, Heng
Lefloch, Damien - Powiązania:
- https://bibliotekanauki.pl/articles/2200761.pdf
- Data publikacji:
- 2022
- Wydawca:
- Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
- Tematy:
-
e-commerce
clothing image classification
traditional machine learning
CNN
HOG
SVM
small VGG network - Opis:
- 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.
- Źródło:
-
Fibres & Textiles in Eastern Europe; 2022, 5 (151); 66--78
1230-3666
2300-7354 - Pojawia się w:
- Fibres & Textiles in Eastern Europe
- Dostawca treści:
- Biblioteka Nauki
Artykuł