INTEL-TAU: A Color Constancy Dataset

Firas Laakom, Jenni Raitoharju, Jarno Nikkanen, Alexandros Iosifidis, Moncef Gabbouj

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.

Original languageEnglish
Article number9371681
Pages (from-to)39560-39567
Number of pages8
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 8 Mar 2021
Publication typeA1 Journal article-refereed

Keywords

  • Color constancy
  • dataset
  • illumination estimation
  • regression

Publication forum classification

  • Publication forum level 2

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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