document: A comparison of computational color constancy Algorithms . II . Experiments with image data For pt.I see ibid. , vol . 11 , no. 9 , p.972-84 -LRB- 2002 -RRB- . We test a number of the leading computational color constancy algorithms using a comprehensive set of images . These were of 33 different scenes under 11 different sources representative of common illumination conditions . The algorithms studied include two gray world methods , a version of the Retinex method , several variants of Forsyth 's -LRB- 1990 -RRB- gamut-mapping method , Cardei et al. 's -LRB- 2000 -RRB- neural net method , and Finlayson et al. 's color by correlation method -LRB- Finlayson et al. 1997 , 2001 ; Hubel and Finlayson 2000 -RRB- . We discuss a number of issues in applying color constancy ideas to image data , and study in depth the effect of different preprocessing strategies . We compare the performance of the algorithms on image data with their performance on synthesized data . All data used for this study are available online at http://www.cs.sfu.ca/~color/data , and implementations for most of the algorithms are also available -LRB- http://www.cs.sfu.ca/~color/code -RRB- . Experiments with synthesized data -LRB- part one of this paper -RRB- suggested that the methods which emphasize the use of the input data statistics , specifically color by correlation and the neural net algorithm , are potentially the most effective at estimating the chromaticity of the scene illuminant . Unfortunately , we were unable to realize comparable performance on real images . Here exploiting pixel intensity proved to be more beneficial than exploiting the details of image chromaticity statistics , and the three-dimensional -LRB- 3-D -RRB- gamut-mapping algorithms gave the best performance
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