Merge pull request #490 from mschoeler:master

modules/ml/doc/support_vector_machines.rst

@@ -115,9 +115,13 @@ The constructors.
 
         * **CvSVM::SIGMOID** Sigmoid kernel: :math:`K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)`.
 
+        * **CvSVM::CHI2** Exponential Chi2 kernel, similar to the RBF kernel: :math:`K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0`.
+
+        * **CvSVM::INTER** Histogram intersection kernel. A fast kernel. :math:`K(x_i, x_j) = min(x_i,x_j)`.
+
     :param degree: Parameter ``degree`` of a kernel function (POLY).
 
-    :param gamma: Parameter :math:`\gamma` of a kernel function (POLY / RBF / SIGMOID).
+    :param gamma: Parameter :math:`\gamma` of a kernel function (POLY / RBF / SIGMOID / CHI2).
 
     :param coef0: Parameter ``coef0`` of a kernel function (POLY / SIGMOID).
 
@@ -142,6 +146,10 @@ The default constructor initialize the structure with following values:
         term_crit = cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
     }
 
+A comparison of different kernels on the following 2D test case with four classes. Four C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three different kernels (CHI2, INTER, RBF). The color depicts the class with max score. Bright means max-score > 0, dark means max-score < 0.
+
+.. image:: pics/SVM_Comparison.png
+
 
 
 CvSVM

modules/ml/include/opencv2/ml/ml.hpp

@@ -297,7 +297,7 @@ struct CV_EXPORTS_W_MAP CvSVMParams
     CV_PROP_RW int         svm_type;
     CV_PROP_RW int         kernel_type;
     CV_PROP_RW double      degree; // for poly
-    CV_PROP_RW double      gamma;  // for poly/rbf/sigmoid
+    CV_PROP_RW double      gamma;  // for poly/rbf/sigmoid/chi2
     CV_PROP_RW double      coef0;  // for poly/sigmoid
 
     CV_PROP_RW double      C;  // for CV_SVM_C_SVC, CV_SVM_EPS_SVR and CV_SVM_NU_SVR
@@ -326,7 +326,10 @@ struct CV_EXPORTS CvSVMKernel
     virtual void calc_non_rbf_base( int vec_count, int vec_size, const float** vecs,
                                     const float* another, float* results,
                                     double alpha, double beta );
-
+    virtual void calc_intersec( int vcount, int var_count, const float** vecs,
+                            const float* another, float* results );    
+    virtual void calc_chi2( int vec_count, int vec_size, const float** vecs,
+                              const float* another, float* results );
     virtual void calc_linear( int vec_count, int vec_size, const float** vecs,
                               const float* another, float* results );
     virtual void calc_rbf( int vec_count, int vec_size, const float** vecs,
@@ -456,7 +459,7 @@ public:
     enum { C_SVC=100, NU_SVC=101, ONE_CLASS=102, EPS_SVR=103, NU_SVR=104 };
 
     // SVM kernel type
-    enum { LINEAR=0, POLY=1, RBF=2, SIGMOID=3 };
+    enum { LINEAR=0, POLY=1, RBF=2, SIGMOID=3, CHI2=4, INTER=5 };
 
     // SVM params type
     enum { C=0, GAMMA=1, P=2, NU=3, COEF=4, DEGREE=5 };

modules/ml/src/svm.cpp

@@ -220,6 +220,8 @@ bool CvSVMKernel::create( const CvSVMParams* _params, Calc _calc_func )
         calc_func = params->kernel_type == CvSVM::RBF ? &CvSVMKernel::calc_rbf :
                     params->kernel_type == CvSVM::POLY ? &CvSVMKernel::calc_poly :
                     params->kernel_type == CvSVM::SIGMOID ? &CvSVMKernel::calc_sigmoid :
+                    params->kernel_type == CvSVM::CHI2 ? &CvSVMKernel::calc_chi2 :
+                    params->kernel_type == CvSVM::INTER ? &CvSVMKernel::calc_intersec :
                     &CvSVMKernel::calc_linear;
 
     return true;
@@ -318,6 +320,52 @@ void CvSVMKernel::calc_rbf( int vcount, int var_count, const float** vecs,
         cvExp( &R, &R );
 }
 
+/// Histogram intersection kernel
+void CvSVMKernel::calc_intersec( int vcount, int var_count, const float** vecs,
+                            const float* another, Qfloat* results )
+{
+    int j, k;
+    for( j = 0; j < vcount; j++ )
+    {
+        const float* sample = vecs[j];
+        double s = 0;
+        for( k = 0; k <= var_count - 4; k += 4 )
+            s += min(sample[k],another[k]) + min(sample[k+1],another[k+1]) +
+                 min(sample[k+2],another[k+2]) + min(sample[k+3],another[k+3]);
+        for( ; k < var_count; k++ )
+            s += min(sample[k],another[k]);
+        results[j] = (Qfloat)(s);
+    }    
+}
+
+/// Exponential chi2 kernel
+void CvSVMKernel::calc_chi2( int vcount, int var_count, const float** vecs,
+                            const float* another, Qfloat* results )
+{	  
+    CvMat R = cvMat( 1, vcount, QFLOAT_TYPE, results );
+    double gamma = -params->gamma;
+    int j, k;
+    for( j = 0; j < vcount; j++ )
+    {
+        const float* sample = vecs[j];
+        double chi2 = 0;
+        for(k = 0 ; k < var_count; k++ ) 
+	{
+            double d = sample[k]-another[k];
+	    double devisor = sample[k]+another[k];
+	    /// if devisor == 0, the Chi2 distance would be zero, but calculation would rise an error because of deviding by zero
+	    if (devisor != 0) 
+	    {	      
+	      chi2 += d*d/devisor;
+	    } 
+	}
+        results[j] = (Qfloat) (gamma*chi2);
+    } 
+    if( vcount > 0 )
+      cvExp( &R, &R );   
+    
+    
+}
 
 void CvSVMKernel::calc( int vcount, int var_count, const float** vecs,
                         const float* another, Qfloat* results )
@@ -1214,7 +1262,8 @@ bool CvSVM::set_params( const CvSVMParams& _params )
     svm_type = params.svm_type;
 
     if( kernel_type != LINEAR && kernel_type != POLY &&
-        kernel_type != SIGMOID && kernel_type != RBF )
+        kernel_type != SIGMOID && kernel_type != RBF && 
+        kernel_type != INTER && kernel_type != CHI2)
         CV_ERROR( CV_StsBadArg, "Unknown/unsupported kernel type" );
 
     if( kernel_type == LINEAR )