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Merge pull request #490 from mschoeler:master

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Andrey Kamaev 2013-02-26 18:33:05 +04:00 committed by OpenCV Buildbot
commit 9f888fdb97
4 changed files with 65 additions and 5 deletions
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modules/ml/doc/pics/SVM_Comparison.png Normal file
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10
modules/ml/doc/support_vector_machines.rst
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@ -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

9
modules/ml/include/opencv2/ml/ml.hpp
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@ -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 };

51
modules/ml/src/svm.cpp
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@ -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 )