diff --git a/Examples/Learning/SVMImageClassificationExample.cxx b/Examples/Learning/SVMImageClassificationExample.cxx
new file mode 100644
index 0000000000000000000000000000000000000000..ce295a20155a6a9ef05427830d213fcb495c863e
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+++ b/Examples/Learning/SVMImageClassificationExample.cxx
@@ -0,0 +1,389 @@
+#if defined(_MSC_VER)
+#pragma warning ( disable : 4786 )
+#endif
+
+#include <fstream>
+
+
+
+
+
+#include "otbImageFileReader.h"
+#include "otbImageFileWriter.h"
+#include "itkImageRegionIterator.h"
+#include "itkRescaleIntensityImageFilter.h"
+#include "otbImage.h"
+
+
+// Software Guide : BeginCommandLineArgs
+// INPUTS: {ROI_QB_MUL_1.png}
+// OUTPUTS: {ROI_QB_MUL_1_SVN_CLASS.png}
+// ${OTB_SOURCE_DIR}/Examples/Data/svm_image_model.svm
+// Software Guide : EndCommandLineArgs
+
+// Software Guide : BeginLatex
+// This example illustrates the use of the
+// \doxygen{otb::SVMClassifier} class for performing SVM
+// classification on images.
+// In this example, we will use an SVM model estimated in the example
+// of section \ref{sec:LearningWithImages}
+// to separate between water and non-water pixels by using the RGB
+// values only. The images used for this example are shown in
+// figure~\ref{fig:SVMROIS}.
+// The first thing to do is include the header file for the
+// class. Since the \doxygen{otb::SVMClassifier} takes
+// \doxygen{itk::ListSample}s as input, the class
+// \doxygen{itk::PointSetToListAdaptor} is needed.\\
+//
+//
+// Software Guide : EndLatex
+
+
+
+#include "itkImageToListAdaptor.h"
+#include "itkListSample.h"
+#include "otbSVMClassifier.h"
+
+
+
+int main(int argc, char* argv[] )
+{
+
+ namespace stat = itk::Statistics ;
+
+ if (argc != 4)
+ {
+ std::cout << "Usage : " << argv[0] << " inputImage outputImage modelFile "
+ << std::endl ;
+ return EXIT_FAILURE;
+ }
+
+ const char * imageFilename = argv[1];
+ const char * modelFilename = argv[3];
+ const char * outputFilename = argv[2];
+
+
+
+// Software Guide : BeginLatex
+//
+// In the framework of supervised learning and classification, we will
+// always use feature vectors for the characterization of the
+// classes. On the other hand, the class labels are scalar
+// values. Here, we start by defining the type of the features as the
+// \code{PixelType}, which will be used to define the feature
+// \code{VectorType}. We also declare the type for the labels.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+ typedef double PixelType;
+ typedef std::vector<PixelType> VectorType;
+ typedef int LabelPixelType;
+
+// Software Guide : EndCodeSnippet
+ const unsigned int Dimension = 2;
+
+
+// Software Guide : BeginLatex
+//
+// We can now proceed to define the image type used for storing the
+// features. We also define the reader.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+ typedef otb::Image< itk::FixedArray<PixelType,3>,
+ Dimension > InputImageType;
+
+
+ typedef otb::ImageFileReader< InputImageType > ReaderType;
+
+// Software Guide : EndCodeSnippet
+
+
+// Software Guide : BeginLatex
+//
+// We can now read the image by calling he \code{Update} method of the reader.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+ ReaderType::Pointer reader = ReaderType::New();
+
+
+ reader->SetFileName( imageFilename );
+
+ reader->Update();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// The image has now to be transformed to a sample which
+// is compatible with the classification framework. We will use a
+// \doxygen{itk::Statistics::ImageToListAdaptor} for this
+// task. This class is templated over the image type used for
+// storing the measures.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+ typedef itk::Statistics::ImageToListAdaptor< InputImageType > SampleType;
+ SampleType::Pointer sample = SampleType::New();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// After instatiantion, we can set the image as an imput of our
+// sample adaptor.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+ sample->SetImage(reader->GetOutput());
+
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// Now, we need to declare the SVM model which is to be used by the
+// classifier. The SVM model is templated over the type of value used
+// for the measures and the type of pixel used for the labels.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+ typedef otb::SVMModel< PixelType, LabelPixelType > ModelType;
+
+ ModelType::Pointer model = ModelType::New();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// After instantiation, we can load a model saved to a file (see
+// section \ref{sec:LearningWithImages} for an example of model
+// estimation and storage to a file.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+ model->LoadModel( modelFilename );
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We have now all the elements to create a classifier. The classifier
+// is templated over the sample type (the type of the data to be
+// classified) and the label type (the type of the ouput of the classifier).
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+ typedef otb::SVMClassifier< SampleType, LabelPixelType > ClassifierType ;
+
+ ClassifierType::Pointer classifier = ClassifierType::New() ;
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We set the classifier parameters : number of classes, SVM model,
+// the sample data. And we trigger the classification process by
+// calling the \code{Update} method.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+ int numberOfClasses = model->GetNumberOfClasses();
+ classifier->SetNumberOfClasses(numberOfClasses) ;
+ classifier->SetModel( model );
+ classifier->SetSample(sample.GetPointer()) ;
+ classifier->Update() ;
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// After the classification step, we usually want to get the
+// results. The classifier gives an output under the form of a sample
+// list. This list supports the classical STL iterators. Therefore, we
+// will create an output image and fill it up with the results of the
+// classification. The pixel type of the output image is the same as
+// the one used for the labels.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+
+ typedef ClassifierType::ClassLabelType OutputPixelType;
+ typedef otb::Image< OutputPixelType, Dimension > OutputImageType;
+
+ OutputImageType::Pointer outputImage = OutputImageType::New();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We allocate the memory for the outpu image using the information
+// from the input image.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+
+ typedef itk::Index<Dimension> myIndexType;
+ typedef itk::Size<Dimension> mySizeType;
+ typedef itk::ImageRegion<Dimension> myRegionType;
+
+ mySizeType size;
+ size[0] = reader->GetOutput()->GetRequestedRegion().GetSize()[0];
+ size[1] = reader->GetOutput()->GetRequestedRegion().GetSize()[1];
+
+ myIndexType start;
+ start[0] = 0;
+ start[1] = 0;
+
+ myRegionType region;
+ region.SetIndex( start );
+ region.SetSize( size );
+
+ outputImage->SetRegions( region );
+ outputImage->Allocate();
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We can now declare the interators on the list that we get at the
+// output of the classifier as well as the iterator to fill the output image.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+
+ ClassifierType::OutputType* membershipSample =
+ classifier->GetOutput() ;
+ ClassifierType::OutputType::ConstIterator m_iter =
+ membershipSample->Begin() ;
+ ClassifierType::OutputType::ConstIterator m_last =
+ membershipSample->End() ;
+
+ typedef itk::ImageRegionIterator< OutputImageType> OutputIteratorType;
+ OutputIteratorType outIt( outputImage,
+ outputImage->GetBufferedRegion() );
+
+ outIt.GoToBegin();
+
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We will iterate through the list, get the labels and assign pixel
+// values to the output image.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+
+ while (m_iter != m_last && !outIt.IsAtEnd())
+ {
+ outIt.Set(m_iter.GetClassLabel());
+ ++m_iter ;
+ ++outIt;
+ }
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// Only for visualization purposes, we choose to rescale the image of
+// classes before sving it to a file. We will use the
+// \doxygen{itk::RescaleIntensityImageFilter} for this purpose.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+
+ typedef otb::Image< unsigned char, Dimension > FileImageType;
+
+
+ typedef itk::RescaleIntensityImageFilter< OutputImageType,
+ FileImageType > RescalerType;
+
+ RescalerType::Pointer rescaler = RescalerType::New();
+
+ rescaler->SetOutputMinimum( itk::NumericTraits< unsigned char >::min());
+ rescaler->SetOutputMaximum( itk::NumericTraits< unsigned char >::max());
+
+ rescaler->SetInput( outputImage );
+
+// Software Guide : EndCodeSnippet
+
+// Software Guide : BeginLatex
+//
+// We can now create an image file writer and save the image.
+//
+// Software Guide : EndLatex
+
+// Software Guide : BeginCodeSnippet
+
+ typedef otb::ImageFileWriter< FileImageType > WriterType;
+
+ WriterType::Pointer writer = WriterType::New();
+
+ writer->SetFileName( outputFilename );
+ writer->SetInput( rescaler->GetOutput() );
+
+ writer->Update();
+// Software Guide : EndCodeSnippet
+
+
+
+// Software Guide : BeginLatex
+// Figure \ref{fig:SVMCLASS} shows the result of the SVM classification.
+// \begin{figure}
+// \center
+// \includegraphics[width=0.35\textwidth]{ROI_QB_MUL_1.eps}
+// \includegraphics[width=0.35\textwidth]{ROI_QB_MUL_1_SVN_CLASS.eps}
+// \itkcaption[SVM Image Classification]{Result of the SVM
+// classification . Left: RGB image. Right: image of classes.}
+// \label{fig:SVMCLASS}
+// \end{figure}
+// Software Guide : EndLatex
+
+ return EXIT_SUCCESS;
+}
+
+
+
+
+
+
+