Abstract
Regression methods have been successfully applied to the area of reflectance estimation. The local linear methods show better generalization performance than the global nonlinear methods in this problem. However, the local linear models which treat every neighbor equally would lose some nonlinear information. To improve the learning ability for the nonlinear structure and reserve the generalization ability of the linear method at the same time, we propose the locally weighted linear regression method for reflectance estimation. The proposed method assigns weights to the neighbors with kernel functions and solves the weighted least squares problem to reconstruct the spectral reflectance. Experiment results show that our approach has better recovery precision and generalization performance than both the global kernel methods and the local linear methods.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (NSFC, grants 61375006) and the Outstanding Young College Teacher Program of Guangdong Province (grant Yq2013032).
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Lu, D., Zhang, W., Cuan, K., Liu, P. (2018). Reflectance Estimation Based on Locally Weighted Linear Regression Methods. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-13-1648-7_8
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DOI: https://doi.org/10.1007/978-981-13-1648-7_8
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