Probabilistic Color Constancy

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions using a graph-based representation of the image. To estimate the weight of each (super-)pixel, we rely on two assumptions: (Super-)pixels with similar colors contribute similarly and darker (super-)pixels contribute less. The resulting system has one global optimum solution. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Print)978-1-7281-6395-6
Publication statusPublished - 1 Oct 2020
Publication typeA4 Article in conference proceedings
EventIEEE International Conference on Image Processing - United Arab Emirates, Abu Dhabi, United Arab Emirates
Duration: 25 Oct 202028 Oct 2020

Publication series

NameProceedings : International Conference on Image Processing
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549


ConferenceIEEE International Conference on Image Processing
Abbreviated titleICIP 2020
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Internet address


  • Image color analysis
  • Lighting
  • Optimization
  • Estimation
  • Computational modeling
  • Task analysis
  • Probabilistic logic
  • Color constancy
  • illumination estimation
  • graph-based learning

Publication forum classification

  • Publication forum level 1


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