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A Perceptual Comparison of Distance Measures for Color Constancy Algorithms

  • Arjan Gijsenij
  • Theo Gevers
  • Marcel P. Lucassen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)

Abstract

Color constancy is the ability to measure image features independent of the color of the scene illuminant and is an important topic in color and computer vision. As many color constancy algorithms exist, different distance measures are used to compute their accuracy. In general, these distances measures are based on mathematical principles such as the angular error and Euclidean distance. However, it is unknown to what extent these distance measures correlate to human vision.

Therefore, in this paper, a taxonomy of different distance measures for color constancy algorithms is presented. The main goal is to analyze the correlation between the observed quality of the output images and the different distance measures for illuminant estimates. The output images are the resulting color corrected images using the illuminant estimates of the color constancy algorithms, and the quality of these images is determined by human observers. Distance measures are analyzed how they mimic differences in color naturalness of images as obtained by humans.

Based on the theoretical and experimental results on spectral and real-world data sets, it can be concluded that the perceptual Euclidean distance (PED) with weight-coefficients (w R  = 0.26, w G  = 0.70, w B  = 0.04) finds its roots in human vision and correlates significantly higher than all other distance measures including the angular error and Euclidean distance.

Keywords

Distance Measure Color Space Human Observer Angular Error Hyperspectral Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Arjan Gijsenij
    • 1
  • Theo Gevers
    • 1
  • Marcel P. Lucassen
    • 1
  1. 1.Intelligent Systems Laboratory AmsterdamUniversity of AmsterdamAmsterdamThe Netherlands

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