Automatic Model Selection by Modelling the Distribution of Residuals

  • T. F. Cootes
  • N. Thacker
  • C. J. Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)


Many problems in computer vision involve a choice of the most suitable model for a set of data. Typically one wishes to choose a model which best represents the data in a way that generalises to unseen data without overfitting. We propose an algorithm in which the quality of a model match can be determined by calculating how well the distribution of model residuals matches a distribution estimated from the noise on the data. The distribution of residuals has two components - the measurement noise, and the noise caused by the uncertainty in the model parameters. If the model is too complex to be supported by the data, then there will be large uncertainty in the parameters. We demonstrate that the algorithm can be used to select appropriate model complexity in a variety of problems, including polynomial fitting, and selecting the number of modes to match a shape model to noisy data.


Model Selection Gaussian Noise Measurement Noise Shape Model Unseen Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • T. F. Cootes
    • 1
  • N. Thacker
    • 1
  • C. J. Taylor
    • 1
  1. 1.Department of Imaging Science and Biomedical EngineeringUniversity of ManchesterManchesterUK

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