Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (04): 117-121.doi: 10.13475/j.fzxb.20180603205

• Apparel Engineering • Previous Articles     Next Articles

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. 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 E-mail:zfy166@zstu.edu.cn

RichHTML

10

PDF (PC)

155

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

CLC Number: 

  • TS941.26

Fig.1

Samples of five women’s trousers. (a) Saggy pants; (b) Broad-legged pants; (c) Flared trousers; (d) Pencil pants; (e) Straight pants"

Fig.2

Framework of convolutional neural network"

Tab.1

Hidden-unit’s parameters of network"

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

Fig.3

Pretreatment process of image. (a) Grey-scale image; (b) Binary image; (c) Skeleton image"

Tab.2

Accuracy of silhouette classification by modified CNN"

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

Tab.3

Accuracy of silhouette classification by FD and SVM"

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
[1] BOSSARD L, DANTONE M, LEISTNER C, et al. Apparel classification with style[C]// Asian Conference on Computer Vision. Korea: Springer-Verlag Vision, 2012: 321-335.
[2] DI W, WAH C, BHARDWAJ A, et al. Style finder: fine-grained clothing style recognition and retri-eval[C]// IEEE Conference on Computer Vision and Pattern Recognition. Portland: IEEE Computer Society Foundation, 2013: 8-10.
[3] ZHANG W, ANTUNEZ E, GOKTURK S, et al. Apparel silhouette attributes recognition [C]//IEEE Workshop on the Applications of Computer Vision. Breckenridge: IEEE Computer Society, 2012: 489-496.
[4] AN L X, LI W. An integrated approach to fashion flat sketches classification[J]. International Journal of Clothing Science and Technology, 2014,26(5):346-366.
[5] 李东, 万贤福, 汪军. 采用傅里叶描述子和支持向量机的服装款式识别方法[J]. 纺织学报, 2017,38(5):122-127.
LI Dong, WAN Xianfu, WANG Jun. Clothing style recognition approach using fourier descriptors and support vector machines[J]. Journal of Textile Research, 2017,38(5):122-127.
[6] YAMAGUCHI K, KIAPOUR H, ORTIZ L, et al. Parsing clothing in fashion photographs[C]// IEEE Conference on Computer Vision and Pattern Recognition. Providence: IEEE Computer Science, 2012: 3570-3577.
[7] ZAGORUYKO S, KOMODAKIS N. Learning to compare image patches via convolutional neural networks[C]// IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE Computer Society, 2015: 4353-4361.
[8] JIA Y, SHELHAMER E, DONAHUE J. Caffe: convolutional architecture for fast feature embedding[C]// 22nd ACM International Conference on Multimedia. Orlando: Computer Science, 2014: 675-678.
[9] KARPATHY A, TODERICI G, SHETTY S, et al. Large-scale video classification with convolutional neural networks[C]// IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE Computer Society, 2014: 1725-1732.
[10] SERMANET P, LECUN Y. Traffic sign recognition with multi-scale convolutional networks[C]// International Joint Conference on Neural Networks. California: International Neural Network Society, 2011: 2809-2813.
[11] CUI X, LIU Y, WANG C, et al. Defect classification for tire X-ray images using convolutional neural network[J]. Electronic Measurement Technology, 2017,40(5):168-173.
[12] CHANG C C. LIN C J, LIBSVM: A library for support vector machines [CP/OL]. 2010-03-11 [2015-11-09]. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[1] LIU Zhengdong, LIU Yihan, WANG Shouren. Depth learning method for suit detection in images [J]. Journal of Textile Research, 2019, 40(04): 158-164.
[2] . Emotion classification of necktie pattern based on convolution neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(08): 117-123.
[3] . Position recognition of spinning yarn breakage based on convolution neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 136-141.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd