Illumination correction of dyed fabrics approach using Bagging-based ensemble particle swarm optimization–extreme learning machine

Zhiyu Zhou; Rui Xu; Dichong Wu; Zefei Zhu; Haiyan Wang

doi:10.1117/1.OE.55.9.093102

9 September 2016 Illumination correction of dyed fabrics approach using Bagging-based ensemble particle swarm optimization–extreme learning machine

Zhiyu Zhou, Rui Xu, Dichong Wu, Zefei Zhu, Haiyan Wang

Author Affiliations +

Optical Engineering, Vol. 55, Issue 9, 093102 (September 2016). https://doi.org/10.1117/1.OE.55.9.093102

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.

Citation Download Citation

Zhiyu Zhou, Rui Xu, Dichong Wu, Zefei Zhu, and Haiyan Wang "Illumination correction of dyed fabrics approach using Bagging-based ensemble particle swarm optimization–extreme learning machine," Optical Engineering 55(9), 093102 (9 September 2016). https://doi.org/10.1117/1.OE.55.9.093102

Published: 9 September 2016

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