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Recognizing Hand-Woven Fabric Pattern Designs Based on Deep Learning

  • Wichai PuarungrojEmail author
  • Narong Boonsirisumpun
Conference paper
  • 239 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 924)

Abstract

Hand-woven fabric pattern designs commonly represent the tradition and culture of local communities. Pattern recognition methods can help classify these pattern designs without having to find an expert. The research aims at recognizing woven fabric pattern designs of traditional fabrics called Phasin in Loei province, Thailand based on deep learning methods. In recognizing pattern design, three deep learning models were experimented: Inception-v3, Inception-v4, and MobileNets. The research collected images of real silk fabrics containing 10 pattern designs. For each pattern, there were 180 images segmented and there were 1,800 images in total in the dataset. The data were trained and tested based on 10-fold cross-validation approach. The results of the test show that MobileNets outperforms Inception-v3 and Inception-v4. The test accuracy rates of pattern design recognition for MobileNets, Inception-v3, and Inception-v4 are 94.19, 92.08, and 91.81%, respectively. The future work will be done by carrying out more data training to increase performance, obtaining more pattern designs, and developing an application based on a high-performance model.

Keywords

Pattern recognition Deep learning Convolutional neural network Weaving pattern Loei Phasin Fabric pattern design 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Computer Science Department, Faculty of Science and TechnologyLoei Rajabhat UniversityLoeiThailand

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