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Unsupervised segmentation and elm for fabric defect image classification

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Abstract

In order to solve the problem of low accuracy and efficiency for surface defects in common woven fabrics, a novel fabric defect classification method is proposed based on unsupervised segmentation and ELM. The classification method is divided into four steps including defect segmentation, feature extraction, ELM classifier training, and Bayesian probability fusion. Firstly, an unsupervised segmentation is presented for the Grayscale fabric defect image after preprocessing. Secondly, geometric and texture features were extracted by using the segmented image and the undivided Grayscale image. Then, features and labels in fabric defect images are considered as training sets to train the ELM classifier. Finally, the input fabric defect image is classified by the trained ELM classifier and the Bayesian probability fusion method. Experimental results show that the proposed method can classify the fabric defect image with high accuracy and efficiency that can better meet the requirements for practical applications.

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Acknowledgements

The authors would like to thank the Associate Editor and the reviewers for their valuable comments and suggestions on this paper. This research is supported by the National Natural Science Foundation of China (61862036, 61462051, 61462056, 81560296), the Applied Fundamental Research Project of Yunnan Province (2017FB097).

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Authors and Affiliations

  1. Computer Technology Application Key Lab of Yunnan Province, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Li Liu, Jianhong Zhang, Lijun Liu & Qingsong Huang

  2. Faculty of Aeronautics, and Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Xiaodong Fu

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  1. Li Liu
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Correspondence to Li Liu.

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Liu, L., Zhang, J., Fu, X. et al. Unsupervised segmentation and elm for fabric defect image classification. Multimed Tools Appl 78, 12421–12449 (2019). https://doi.org/10.1007/s11042-018-6786-7

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