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

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.

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

“…Additionally, a random forest model utilizing product titles achieved superior accuracy, compared to SVM and CNN, for the classification of e-commerce products [187]. On the other hand, image recognition techniques have been extensively explored in the field of fashion and clothing product classification, employing various machine learning and deep learning models such as CNN, CNN-RNN, transfer learning, SVM-CNN, and LSTM to accurately categorize products [188][189][190][191][192][193][194][195][196]. These studies emphasize the vital role of advanced machine learning and deep learning techniques in enhancing product classification, image recognition, and categorization in the e-commerce industry.…”

An improved image processing algorithm for visual characteristics in graphic design

“…With the development and maturity of computer technology, researchers began to mine clothing-related information from clothing plane image data, in which line drawings and color points are important clothing information, which is composed of clothing structure, color, texture, and other elements, and can represent the overall visual experience of clothing ( Zhou et al, 2022a ). Therefore, how to use image processing technology to extract various elements from clothing images to help clothing designers grasp the trend and draw a more stylish clothing plan has gradually become a research hotspot ( Xu et al, 2022 ).…”