Examples of jMEF.MixtureModel

jMEF.MixtureModel
@author Vincent Garcia @author Frank Nielsen @version 1.0 @section LicenseSee file LICENSE.txt @section DescriptionA mixture model is a powerful framework commonly used to estimate the probability density function (PDF) of a random variable. Let us consider a mixture model \f$f\f$ of size \f$n\f$. The probability density function \f$f\f$ evaluated at \f$x \in R^d\f$ is given by \f[ f(x) = \sum_{i=1}^n \alpha_i f_i(x)\f] where \f$\alpha_i \in [0,1]\f$ denotes the weight of the \f$i^{\textrm{th}}\f$ mixture component \f$f_i\f$ such as \f$\sum_{i=1}^n \alpha_i=1\f$. The MixtureModel class provides a convenient way to create and manage mixture of exponential families.

   * @param   image  input image
   * @param   n      number of components in the mixture model
   * @return         a mixture of Gaussian of n components computed from the input image
   */
  public static MixtureModel loadMixtureModel(String path, BufferedImage image, int n){
    MixtureModel mm = MixtureModel.load(path);
    if (mm==null){
      PVector[]         px       = convertColorImageToPointSet3D(image);
      Vector<PVector>[] clusters = KMeans.run(px, n);
      mm = BregmanSoftClustering.initialize(clusters, new MultivariateGaussian());
      mm = BregmanSoftClustering.run(px, mm);

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    String mixture_path  = String.format("%s%s_5D_%03d.mix", input_folder, image_name, n); 
    
    // Read image and generate initial mixture model
    System.out.print("Read image and generate/load the mixture 5D : ");
    BufferedImage image = Image.readImage(image_path);
    MixtureModel  f     = Image.loadMixtureModel(mixture_path, image, n);
    System.out.println("ok");
    
    // Creates and save the statistical image
    System.out.print("Create the statistical image                : ");
    BufferedImage stat = Image.createImageFromMixtureModel(image.getWidth(), image.getHeight(), f);

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