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Optimal selection of representative samples for efficient digital camera‐based spectra recovery
Author(s) -
Liang Jinxing,
Zhu Qiang,
Liu Qiang,
Xiao Kaida
Publication year - 2022
Publication title -
color research and application
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.393
H-Index - 62
eISSN - 1520-6378
pISSN - 0361-2317
DOI - 10.1002/col.22718
Subject(s) - robustness (evolution) , computer science , rgb color model , digital camera , workload , artificial intelligence , selection (genetic algorithm) , computer vision , database , pattern recognition (psychology) , data mining , chemistry , biochemistry , gene , operating system
For digital camera‐based spectra recovery, the spectral reflectance of the object being imaged always needs to be accurately recovered using training samples from available database. Considering the heavy workload when using all samples in database as training samples in practice, a new representative samples selection method is proposed for efficient digital camera‐based spectra recovery based on single RGB image. The representative simulation system is firstly constructed through correlation analysis of spectra recovery results of different systems, and based on the representative simulation system, a few number of representative samples are selected from the database based on minimum of the defined simulate spectra recovery error. The effectiveness of the proposed method is evaluated and compared with existing method. As the results show, the proposed method outperforms the existing methods, and the robustness of the selected representative samples is consistent with the database in practical applications.