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

Hemrit, Ghalia, Finlayson, Graham David, Gijsenij, Arjan, Gehler, Peter Vincent, Bianco, Simone, Drew, Mark, Funt, Brian and Shi, Lilong (2019) Providing a single ground-truth for illuminant estimation for the ColorChecker dataset. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14 (8). pp. 1-3. ISSN 0162-8828

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Abstract

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.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 31 Oct 2019 15:43
Last Modified: 14 Nov 2020 01:10
URI: https://ueaeprints.uea.ac.uk/id/eprint/72829
DOI: 10.1109/TPAMI.2019.2919824

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