Color Constancy Convolutional Autoencoder

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

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

5 Citations (Scopus)


In this paper, we study the importance of pretraining for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Number of pages6
ISBN (Electronic)9781728124858
ISBN (Print)978-1-7281-2486-5
Publication statusPublished - 2019
Publication typeA4 Article in conference proceedings
EventIEEE Symposium Series on Computational Intelligence -
Duration: 1 Jan 1900 → …


ConferenceIEEE Symposium Series on Computational Intelligence
Abbreviated titleIEEE SSCI
Period1/01/00 → …


  • color constancy
  • convolutional autoencoders
  • illumination
  • pre-training

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Modelling and Simulation


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