diff --git a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
index e9f9346e3c284ce6a7be0a967afca816eeb072af..eabdc21d31b5e009c5b56541d57188002c4194f5 100644
--- a/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
+++ b/Modules/Learning/Supervised/include/otbLibSVMMachineLearningModel.txx
@@ -74,6 +74,8 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
   m_SVMestimator->SetTrainingSampleList(this->GetTargetListSample());
 
   m_SVMestimator->Update();
+
+  this->m_ConfidenceIndex = m_DoProbabilityEstimates;
 }
 
 template <class TInputValue, class TOutputValue>
@@ -96,6 +98,23 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
       {
       itkExceptionMacro("Confidence index not available for this classifier !");
       }
+    typename SVMEstimatorType::ModelType::ProbabilitiesVectorType probaVector =
+        m_SVMestimator->GetModel()->EvaluateProbabilities(mfunctor(input));
+    double maxProb = 0.0;
+    double secProb = 0.0;
+    for (unsigned int i=0 ; i<probaVector.Size() ; ++i)
+      {
+      if (maxProb < probaVector[i])
+        {
+          secProb = maxProb;
+          maxProb = probaVector[i];
+        }
+      else if (secProb < probaVector[i])
+        {
+          secProb = probaVector[i];
+        }
+      }
+    (*quality) = static_cast<ConfidenceValueType>(maxProb - secProb);
     }
 
   return target;
@@ -115,6 +134,8 @@ LibSVMMachineLearningModel<TInputValue,TOutputValue>
 ::Load(const std::string & filename, const std::string & itkNotUsed(name))
 {
   m_SVMestimator->GetModel()->LoadModel(filename.c_str());
+
+  this->m_ConfidenceIndex = m_SVMestimator->GetModel()->HasProbabilities();
 }
 
 template <class TInputValue, class TOutputValue>