Chengcheng Huang, Jian Yuan, Xiao Qin
Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, China.
Guangxi International Businiess Vocational College, Nanning, China.
Front Comput Neurosci. 2022 Apr 11;15:766284. doi: 10.3389/fncom.2021.766284. eCollection 2021.
With the rapid development of apparel e-commerce, the variety of apparel is increasing, and it becomes more and more important to classify the apparel according to its collar design. Traditional image processing methods have been difficult to cope with the increasingly complex image backgrounds. To solve this problem, an EMRes-50 classification algorithm is proposed to solve the problem of garment collar image classification, which is designed based on the ECA-ResNet50 model combined with the MC-Loss loss function method. Applying the improved algorithm to the Coller-6 dataset, and the classification accuracy obtained was 73.6%. To further verify the effectiveness of the algorithm, it was applied to the DeepFashion-6 dataset, and the classification accuracy obtained was 86.09%. The experimental results show that the improved model has higher accuracy than the existing CNN model, and the model has better feature extraction ability, which is helpful to solve the problem of the difficulty of fine-grained collar classification and promote the further development of clothing product image classification.
随着服装电子商务的快速发展，服装种类日益增多，根据领口设计对服装进行分类变得越来越重要。传统的图像处理方法已难以应对日益复杂的图像背景。为解决这一问题，提出了一种EMRes-50分类算法来解决服装领口图像分类问题，该算法基于ECA-ResNet50模型并结合MC-Loss损失函数方法设计。将改进后的算法应用于Coller-6数据集，获得的分类准确率为73.6%。为进一步验证该算法的有效性，将其应用于DeepFashion-6数据集，获得的分类准确率为86.09%。实验结果表明，改进后的模型比现有CNN模型具有更高的准确率，且该模型具有更好的特征提取能力，有助于解决细粒度领口分类困难的问题，推动服装产品图像分类的进一步发展。
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