Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models
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Analysis-of-Clothing-Image-Classification-Models-A-Comparison-Study-between-Traditional-Machine-Learning-and-Deep-Learning-Models.pdf (2.11 MB)
0000-0002-0966-399X Date
2022-12-22
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Sciendo
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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.
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Xu, Jun, et al. "Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models" Fibres & Textiles in Eastern Europe, vol. 30, no. 5, Sciendo, 2022, pp. 66-78. https://doi.org/10.2478/ftee-2022-0046