Machine learning approach to color constancy

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 ne...

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Bibliographic Details
Published in:Neural networks 2007-07, Vol.20 (5), p.559-563
Main Authors: Agarwal, Vivek, Gribok, Andrei V., Abidi, Mongi A.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Color constancy
Color Perception
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Exact sciences and technology
Learning and adaptive systems
Neural networks
Pattern Recognition, Automated - methods
Regression Analysis
Ridge regression
Support vector regression
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2007.02.004