Color reproduction method by support vector regression for color computer vision

Bo Yang, Hung Yu Chou, Tsung Hsun Yang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In the color computer vision system, the nonlinearity of the camera and computer screen may result in different colors between the screen and the actual color of objects, which requires for color calibration. In this paper, support vector regression (SVR) method was introduced to reproduce the colors of the nonlinear imaging system. Firstly, successive 3σ method was used to eliminate the large errors found in the color measurement. Then, based on the training set measured in advance, SVR model of RBF kernel was applied to map the nonlinear imaging system. In this step, two important parameters (C, γ) were optimized by the Least Mean Squared Validating Errors algorithm to get the best SVR model. Finally, this optimized model could predict the real values displayed on the screen. Compared with quadratic polynomial regression, BP neural network and relevance vector machine, the optimized SVR model has better ability in color reproduction performance and generalization.

Original languageEnglish
Pages (from-to)5649-5656
Number of pages8
Issue number22
StatePublished - Nov 2013


  • Color reproduction
  • Least mean squared validating errors
  • Successive 3σ filter
  • Support vector regression


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