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Intelligent Cashmere/Wool Classification with Convolutional Neural Network

  • Fei Wang
  • Xiangyu Jin
  • Wei Luo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

It is generally believed that there are subtle differences in textures and diameters, between cashmere and wool fibers. Thus, automatically classifying the cashmere/wool fiber images remains a major challenge to the textile industry. In this proposal, we introduced a method that uses Convolutional Neural Networks (CNNs) to identify the two kinds of animal fibers. Specifically, a typical CNN was used to extract image features at first step. Then a region proposal strategy (RPS) was used to localize the fine-grained features from the images. We fine-tuned the CNN model by using the features selected by RPS. Experiments on cashmere/wool image set compared to different models verified the effectiveness of the proposed method for feature extraction.

Keywords

Cashmere/wool Subtle differences Classification Convolutional neural networks 

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61702197, in part by the Natural Science Foundation of Guangdong Province under Grant 2017A030310261, in part by the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education under Grant JYB201708.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Donghua UniversityShanghaiChina
  2. 2.South China Agricultural UniversityGuangzhouChina
  3. 3.Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing University of Science and TechnologyNanjingChina

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