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Image recognition using multi-layer sparse feature extraction with ADMM
Author(s): Tomoya Hirakawa; Kuntopng Wararatpanya; Yoshimistu Kuroki
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Paper Abstract

Being motivated by the Saak (Subspace approximation with augmented kernels) transform, we propose an image recognition scheme using multi-layer sparse feature extraction with a convex solver ADMM (Alternating Direction Method of Multipliers). The Saak transform consists of a multi-layer PCA (Principal Component Analysis) and S/P (Sign-to-Position) conversion to avoid sign confusion. This paper adopts sparse representation instead of PCA and also compares the S/P conversion with the activation function ReLU (Rectified Linear Unit), which is realized by involving the projection mapping onto the non-negative set in convex formulas. The Saak transform uses PCA not only for feature extraction but also for dimension compression of feature vectors. We expect that our method does not need the dimension compression since sparse representation compresses features more than PCA. Experimental results on the MNIST and Fashion-MNIST dataset show that the proposed method is equivalent to the Saak transform in recognition accuracy, and that our method can make features more sparse and extract features that have high discriminant power locally.

Paper Details

Date Published: 22 March 2019
PDF: 5 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110493W (22 March 2019); doi: 10.1117/12.2521348
Show Author Affiliations
Tomoya Hirakawa, National Institute of Technology, Kurume College (Japan)
Kuntopng Wararatpanya, King Mongkut's Institute of Technology Ladkrabang (Thailand)
Yoshimistu Kuroki, National Institute of Technology, Kurume College (Japan)


Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)

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