Reflective color reduction using genetic algorithm optimization | Zendy

Xu Zhiling | Zendy; Brill Michael H. | Zendy

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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.

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