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| Clothing image classification based on multi-scale SE-Xception |
Qiao-hong CHEN( ),YI CHEN,Wen-shu Li,Yu-bo JIA |
| School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China |
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Abstract The current newer and better convolutional neural network Xception was used as the foundation network structures. Multi-scale depth separable convolution was employed to improve the richness of model feature information. The SE-Net model was embedded in the model to enhance the useful feature channels and weaken the useless feature channels. The experimental results show that the multi-scale SE-Xception model achieved good performance in two different noise clothing datasets. The average classification accuracy of ACS dataset was 78.34%, which was higher than VGG-16, ResNet-50 and Xception models by 8.52%, 4.81% and 3.69%, respectively. Therefore, it’s verified that the multi-scale SE-Xception model has better ability to extract features, can obtain more clothing information, and thus improve the clothing image classification effect. To some extent, a multi-scale SE-Xception network is conducive to solve the single feature scale and low information richness problems.
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Received: 14 August 2019
Published: 22 September 2020
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多尺度SE-Xception服装图像分类
应用当前较新颖且分类性能靠前的卷积神经网络Xception作为基础网络结构，尝试采用多尺度的深度可分离卷积来提升模型特征信息的丰富度，在模型中嵌入SE-Net模块增强有用特征通道，减弱无用特征通道. 实验结果表明：提出的多尺度SE-Xception模型在2种噪声程度不同的服装数据集中均取得不错表现；ACS数据集的平均分类准确率为78.34%，分别高于VGG-16、ResNet-50和Xception模型8.52%、4.81%、3.69%；验证了多尺度SE-Xception模型具有更好的特征提取能力，能够提取到更多的服装信息，从而提高服装图像分类效果，一定程度上解决了特征尺度单一、信息丰富度低的问题.
关键词：
服装图像分类,
多尺度SE-Xception,
图像识别,
深度学习,
机器学习,
卷积神经网络（CNN）
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