纺织学报 ›› 2019, Vol. 40 ›› Issue (04): 117-121.doi: 10.13475/j.fzxb.20180603205

• 服装工程 • 上一篇    下一篇

采用卷积神经网络CaffeNet 模型的女裤廓形分类

  

  • 收稿日期:2018-06-07 修回日期:2019-01-08 出版日期:2019-04-15 发布日期:2019-04-16

Classification of women′s trousers silhouette using convolution neural network CaffeNet model

  • Received:2018-06-07 Revised:2019-01-08 Online:2019-04-15 Published:2019-04-16

摘要:

针对服装廓形分类特征提取计算复杂、分类效果尚不理想等问题,提出了一种基于卷积神经网络CaffeNet模型的服装廓形分类方法。以女裤为例,首先建立一个包括吊裆裤、阔腿裤、喇叭裤、小脚裤和直筒裤的5 种女裤廓形样本库,利用卷积神经网络相互交替的卷积层和池化层从女裤图像中自动提取形状特征,通过反向传播算法不断逐层更新权值,采用梯度下降法并且改进全连接层的参数最小化损失函数,运用Softmax 回归分类器来实现女裤的廓形分类。实验结果表明,该方法能够有效地对女裤廓形进行分类,分类准确率达到95%以上,可为服装商品的可视化分类识别提供有效途径。

关键词: 卷积神经网络, CaffeNet 模型, 女裤廓形, Softmax 回归

Abstract:

Aiming at the complicated calculation of clothing silhouette classification feature extraction and poor classification effect, a classification approach of clothing silhouette based on the CaffeNet model of convolution neural network was proposed. Taking women′s trousers as an example, a sample database of five kinds of women′s trousers with silhouette was established at first, comprising saggy pants, broadlegged pants, flared trousers, pencil pants and straight pants, then shape features were extracted automatically from the clothing images using the alternating convolution and pool layers, weight values were updated by back propagation algorithm layer by layer, the gradient descent method was adopted and the parameter of the whole connection layer was modified to minimize loss function, and Softmax regression was used to classify the women′s trousers silhouette. The experimental results show that the novel approach can classify the silhouette of women′s trousers accurately, and the classification accuracy is up to 95%. It can provide an effective way for visual classification and recognition of clothing products.

Key words: convolution neural network, CaffeNet model, women′ s trousers silhouette, Softmax regression

[1] 刘正东 刘以涵 王首人. 西装识别的深度学习方法[J]. 纺织学报, 2019, 40(04): 158-164.
[2] 汪珊娜 张华熊 康锋. 基于卷积神经网络的领带花型情感分类[J]. 纺织学报, 2018, 39(08): 117-123.
[3] 王雯雯 高畅 刘基宏. 应用卷积神经网络的细纱断纱锭位识别[J]. 纺织学报, 2018, 39(06): 136-141.
[4] 景军锋 范晓婷 李鹏飞 洪良. 应用深度卷积神经网络的色织物缺陷检测[J]. 纺织学报, 2017, 38(02): 68-74.
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