Feature Fusion Multi＿XMNet Convolution Neural Network for Clothing Image Classification

作者：周洪雷^*; 彭志飞; 陶然^*; 张璐

来源：Journal of Dong Hua University (english Edition), 2021, 38(06): 519-526.

DOI：10.19884/j.1672-5220.202101013

Faced with the massive amount of online shopping clothing images, how to classify them quickly and accurately is a challenging task in image classification. In this paper, we propose a novel method, named MultiXMNet, to solve the clothing images classification problem. The proposed method mainly consists of two convolution neural network(CNN) branches. One branch extracts multiscale features from the whole expressional image by MultiX which is designed by improving the Xception network, while the other extracts attention mechanism features from the whole expressional image by MobileNetV3-small network. Both multiscale and attention mechanism features are aggregated before making classification. Additionally, in the training stage, global average pooling(GAP), convolutional layers, and softmax classifiers are used instead of the fully connected layer to classify the final features, which speed up model training and alleviate the problem of overfitting caused by too many parameters. Experimental comparisons are made in the public DeepFashion dataset. The experimental results show that the classification accuracy of this method is 95.38%, which is better than InceptionV3, Xception and InceptionV3Xception by 5.58%, 3.32%, and 2.22%, respectively. The proposed MultiXMNet image classification model can help enterprises and researchers in the field of clothing e-commerce to automaticly, efficiently and accurately classify massive clothing images.

• 出版日期2021
• 单位东华大学