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CU-Net: Component Unmixing Network for Textile Fiber Identification

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Abstract

Image-based nondestructive textile fiber identification is a challenging computer vision problem, that is practically useful in fashion, decoration, and design. Although deep learning now outperforms humans in many scenarios such as face and object recognition, image-based fiber identification is still an open problem for deep learning given imbalanced sample and small sample size samples. In this paper, we propose the Component Unmixing Network (CU-Net) for nondestructive textile fiber identification. CU-Net learns effective representations given imbalanced sample and small sample size samples to achieve high-performance textile fiber identification. CU-Net comprises a Deep Feature Extraction Module (DFE-Module) and a Component Unmixing Module (CU-Module). Initially, mixed deep features are extracted by DFE-Module from the input textile patches. Then, CU-Module is employed to extract unmixed representations of different fibers from the mixed deep features. In CU-Module, we introduce a self-interchange and a restraining loss to reduce the mixture between representations of different fibers. Furthermore, we extend CU-Net to the proportion analysis task with very good effect. Extensive experiments demonstrate that: (1) self-interchange and the restraining loss effectively unmix different fiber representations and improve fiber identification accuracy; and (2) CU-Net achieves more accurate fiber identification than the current state-of-the-art multi-label classification methods.

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Acknowledgements

This work is supported by National Key Research and Development Program (2016YFB1200203), National Natural Science Foundation of China (61572428, U1509206), Key Research and Development Program of Zhejiang Province (2018C01004), and the Program of International Science and Technology Cooperation (2013DFG12840), Project of Science and Technology Research and Development Program of China RailwayCorporation (P2018X002), Fundamental Research Funds for the Central Universities.

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Correspondence to Mingli Song.

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Communicated by Cristian Sminchisescu.

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Feng, Z., Liang, W., Tao, D. et al. CU-Net: Component Unmixing Network for Textile Fiber Identification. Int J Comput Vis 127, 1443–1454 (2019). https://doi.org/10.1007/s11263-019-01199-9

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