Colour-appearance modeling using feedforward networks with bayesian regularization method. Part II: Reverse model

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2 Citations (Scopus)


In Part I of this article, the development of a multilayer perceptrons feedforward artificial neural network model to predict colour appearance front colorimetric values was reported. Bayesian regularization was employed for the training of the network. In this part of the article, the reverse model, that is, the perdition of colorimetric values from the colour appearance attributes is reported using the same neural network design methodology developed in Part I. This study should contribute to the building of an artificial neural network-based colour appearance prediction, both forward and reverse, using the most comprehensive LUTCHI colour appearance data sets for training and testing. Good prediction accuracy and generalization ability were obtained using the neural networks built in the study. Because the neural network approach is of a black-box type colour appearance prediction using this method should be easier to apply in practice. Col. Res. Appl.
Original languageEnglish
Pages (from-to)116-121
Number of pages6
JournalColor Research and Application
Issue number2
Publication statusPublished - 1 Apr 2002


  • Back-propagation
  • Bayesian regularization
  • Colour appearance models
  • Feedforward neural networks

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Chemistry(all)
  • Chemical Engineering(all)

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