Illumination correction of dyeing products based on Grey-Edge and kernel extreme learning machine

Abstract

Changes in illumination will result in serious color difference evaluation error in the process of textile printing. In order to solve the problem, a novel illuminant estimation method based on kernel extreme learning machine (KELM) is proposed. Furthermore, a new efficient and low dimensional color feature extraction method based on Grey-Edge framework is adopted to replace the traditional high dimensional binary chromaticity histogram, which is used to represent the input data of KELM. The experiments show that the proposed color constancy method performs better than the traditional support vector regression (SVR) and basic extreme learning machine (ELM) based color constancy methods. Compared with SVR and ELM, the proposed method reduces the median and root mean square errors with approximately 6%, 11%, 43% and 48%, respectively.