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A Holographic Diffraction Label Recognition Algorithm Based on Fusion Double Tensor Features

Li Li1, Chen Cui1,2, Jianfeng Lu1, Shanqing Zhang1,*, Ching-Chun Chang3

1 Hangzhou Dianzi University, Hangzhou, 310018, China
2 Zhejiang Police College, Hangzhou, 310018, China
3 University of Warwick, Coventry, CV4 7AL, UK

* Corresponding Author: Shanqing Zhang. Email:

Computer Systems Science and Engineering 2021, 38(3), 291-303.


As an efficient technique for anti-counterfeiting, holographic diffraction labels has been widely applied to various fields. Due to their unique feature, traditional image recognition algorithms are not ideal for the holographic diffraction label recognition. Since a tensor preserves the spatiotemporal features of an original sample in the process of feature extraction, in this paper we propose a new holographic diffraction label recognition algorithm that combines two tensor features. The HSV (Hue Saturation Value) tensor and the HOG (Histogram of Oriented Gradient) tensor are used to represent the color information and gradient information of holographic diffraction label, respectively. Meanwhile, the tensor decomposition is performed by high order singular value decomposition, and tensor decomposition matrices are obtained. Taking into consideration of the different recognition capabilities of decomposition matrices, we design a decomposition matrix similarity fusion strategy using a typical correlation analysis algorithm and projection from similarity vectors of different decomposition matrices to the PCA (Principal Component Analysis) sub-space , then, the sub-space performs KNN (K-Nearest Neighbors) classification is performed. The effectiveness of our fusion strategy is verified by experiments. Our double tensor recognition algorithm complements the recognition capability of different tensors to produce better recognition performance for the holographic diffraction label system.


Cite This Article

L. Li, C. Cui, J. Lu, S. Zhang and C. Chang, "A holographic diffraction label recognition algorithm based on fusion double tensor features," Computer Systems Science and Engineering, vol. 38, no.3, pp. 291–303, 2021.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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