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Reflective color reduction using genetic algorithm optimization
Author(s) -
Xu Zhiling,
Brill Michael H.
Publication year - 2019
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.22361
Subject(s) - genetic algorithm , reduction (mathematics) , computer science , selection (genetic algorithm) , transformation (genetics) , set (abstract data type) , matrix (chemical analysis) , artificial intelligence , transformation matrix , sample (material) , algorithm , measure (data warehouse) , pattern recognition (psychology) , computer vision , mathematics , machine learning , data mining , materials science , biochemistry , chemistry , geometry , physics , kinematics , classical mechanics , chromatography , composite material , gene , programming language
A subset of colors often needs to be selected to represent a full set. In one such application, a multi‐band color sensor is used to measure reflective color samples, and a matrix transformation method is used to recover the reflectance spectrum of the measured sample. To achieve this, a group of training colors needs to be selected to calculate the transformation matrix. A genetic algorithm (GA) has been developed to optimize the selection of the subset of training colors, and the result is compared with those obtained using random selection or a traditional culling algorithm. In a simulation study, the GA gives better results.