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Fabric Identification Using Convolutional Neural Network

  • Xin Wang
  • Ge Wu
  • Yueqi Zhong
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

Image-based fabric retrieval technique can help to develop new fabrics and manage products. Efficiently extracting features from fabric images is the key to enhance the practicality of this technology. In this paper, convolutional neural network is trained with a dataset of 19,894 different yarn-dyed fabric patterns. Center loss architecture is added to further improve the discriminative power of the network. By properly sampling from original images, the network model can efficiently extract discriminative features and achieve a retrieval accuracy of 99.89% on our test set. This performance maintains well when simpler deep architecture is used, but decreases quickly if the contents of fed fabric image are reduced.

Keywords

Fabric retrieval Convolutional neural network Similarity learning 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No. 61572124).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Donghua UniversityShanghaiChina
  2. 2.Key Lab of Textile Science and TechnologyMinistry of EducationShanghaiChina

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