Neural networks letter: Machine learning approach to color constancy

Authors:
Vivek Agarwal;Andrei V. Gribok;Mongi A. Abidi
Affiliations:
400 Central Drive, School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907, United States;BHSAI/MRMC, Attn: MCMR-ZB-T, Building 363 Miller Dr., Fort Detrick, MD 21792-5012, United States;1508 Ferris Hall, Electrical and Computer Engineering, The University of Tennessee, Knoxville, TN 37996, United States
Venue:
Neural Networks
Year:
2007

Citing 4
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Abstract

A number of machine learning (ML) techniques have recently been proposed to solve color constancy problem in computer vision. Neural networks (NNs) and support vector regression (SVR) in particular, have been shown to outperform many traditional color constancy algorithms. However, neither neural networks nor SVR were compared to simpler regression tools in those studies. In this article, we present results obtained with a linear technique known as ridge regression (RR) and show that it performs better than NNs, SVR, and gray world (GW) algorithm on the same dataset. We also perform uncertainty analysis for NNs, SVR, and RR using bootstrapping and show that ridge regression and SVR are more consistent than neural networks. The shorter training time and single parameter optimization of the proposed approach provides a potential scope for real time video tracking application.