采用卷积神经网络 CaffeNet 模型的女裤廓形分类

doi:10.13475/j.fzxb.20180603205

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

采用卷积神经网络 CaffeNet 模型的女裤廓形分类

吴欢¹, 丁笑君¹^,², 李秦曼¹, 杜磊¹^,², 邹奉元¹^,²()

1.浙江理工大学服装学院, 浙江杭州 310018
2.浙江理工大学浙江省服装工程技术研究中心, 浙江杭州 310018

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
通讯作者: 邹奉元
作者简介:吴欢(1994—),女,硕士生。主要研究方向为服装数字化技术。
基金资助:
“浙江省服装工程技术研究中心”省部级重点实验室开放基金项目(2018FZKF13);2018年浙江省大学生科技创新活动计划项目(2018R406076)

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

WU Huan¹, DING Xiaojun¹^,², LI Qinman¹, DU Lei¹^,², ZOU Fengyuan¹^,²()

1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
2. Zhejiang Provincial Research Center of Clothing Engineering Technology,Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China

Received:2018-06-07 Revised:2019-01-08 Online:2019-04-15 Published:2019-04-16
Contact: ZOU Fengyuan

摘要/Abstract

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, broad-legged 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

中图分类号:

TS941.26

吴欢, 丁笑君, 李秦曼, 杜磊, 邹奉元. 采用卷积神经网络 CaffeNet 模型的女裤廓形分类[J]. 纺织学报, 2019, 40(04): 117-121.

WU Huan, DING Xiaojun, LI Qinman, DU Lei, ZOU Fengyuan. Classification of women’s trousers silhouette using convolution neural network CaffeNet model[J]. Journal of Textile Research, 2019, 40(04): 117-121.

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层数	每层类型	卷积核大小(个数)	步长
1	卷积层1	11×11核(96个)	4
2	池化层1	3×3核(1个)	2
3	卷积层2	5×5核(256个)	1
4	池化层2	3×3核(1个)	2
5	卷积层3	3×3核(384个)	1
6	卷积层4	3×3核(384个)	1
7	卷积层5	3×3核(256个)	1
8	池化层5	3×3核(1个)	2
9	全连接层6	1×1核(1个)	1
10	全连接层7	1×1核(1个)	1
11	全连接层8	1×1核(1个)	1

女裤种类	训练集数量/张	测试集数量/张	准确率/%
吊裆裤	240	60	95.0
阔腿裤	240	60	96.7
喇叭裤	240	60	96.7
小脚裤	240	60	98.3
直筒裤	240	60	95.0

女裤种类	训练集数量/张	测试集数量/张	准确率/%
吊裆裤	240	60	85.0
阔腿裤	240	60	90.0
喇叭裤	240	60	88.3
小脚裤	240	60	88.3
直筒裤	240	60	86.7

采用卷积神经网络 CaffeNet 模型的女裤廓形分类

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

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