Providing a single ground-truth for illuminant estimation for the ColorChecker dataset

Ghalia Hemrit, Graham David Finlayson, Arjan Gijsenij, Peter Vincent Gehler, Simone Bianco, Mark Drew, Brian Funt, Lilong Shi

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
43 Downloads (Pure)


The ColorChecker dataset is one of the most widely used image sets for evaluating and ranking illuminant estimation algorithms. However, this single set of images has at least 3 different sets of ground-truth (i.e. correct answers) associated with it. In the literature it is often asserted that one algorithm is better than another when the algorithms in question have been tuned and tested with the different ground-truths. In this short correspondence we present some of the background as to why the 3 existing ground-truths are different and go on to make a new single and recommended set of correct answers. Experiments reinforce the importance of this work in that we show that the total ordering of a set of algorithms may be reversed depending on whether we use the new or legacy ground-truth data.
Original languageEnglish
Pages (from-to)1-3
Number of pages3
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number8
Publication statusPublished - 1 Jul 2019

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