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Optical Engineering

Illumination correction of dyed fabrics approach using Bagging-based ensemble particle swarm optimization–extreme learning machine
Author(s): Zhiyu Zhou; Rui Xu; Dichong Wu; Zefei Zhu; Haiyan Wang
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Paper Abstract

Changes in illumination will result in serious color difference evaluation errors during the dyeing process. A Bagging-based ensemble extreme learning machine (ELM) mechanism hybridized with particle swarm optimization (PSO), namely Bagging–PSO–ELM, is proposed to develop an accurate illumination correction model for dyed fabrics. The model adopts PSO algorithm to optimize the input weights and hidden biases for the ELM neural network called PSO–ELM, which enhances the performance of ELM. Meanwhile, to further increase the prediction accuracy, a Bagging ensemble scheme is used to construct an independent PSO–ELM learning machine by taking bootstrap replicates of the training set. Then, the obtained multiple different PSO–ELM learners are aggregated to establish the prediction model. The proposed prediction model is evaluated with real dyed fabric images and discussed in comparison with several related methods. Experimental results show that the ensemble color constancy method is able to generate a more robust illuminant estimation model with better generalization performance.

Paper Details

Date Published: 9 September 2016
PDF: 12 pages
Opt. Eng. 55(9) 093102 doi: 10.1117/1.OE.55.9.093102
Published in: Optical Engineering Volume 55, Issue 9
Show Author Affiliations
Zhiyu Zhou, Zhejiang Sci-Tech Univ. (China)
Rui Xu, Zhejiang Sci-Tech Univ. (China)
Dichong Wu, Zhejiang Univ. of Finance and Economics (China)
Zefei Zhu, Hangzhou Dianzi Univ. (China)
Haiyan Wang, Zhejiang Police Vocational Academy (China)

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