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- Title
CU-Net: Component Unmixing Network for Textile Fiber Identification.
- Authors
Feng, Zunlei; Liang, Weixin; Tao, Daocheng; Sun, Li; Zeng, Anxiang; Song, Mingli
- 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.
- Subjects
DEEP learning; COMPUTER vision; OBJECT recognition (Computer vision); IMAGE recognition (Computer vision); TASK analysis; FEATURE extraction
- Publication
International Journal of Computer Vision, 2019, Vol 127, Issue 10, p1443
- ISSN
0920-5691
- Publication type
Article
- DOI
10.1007/s11263-019-01199-9