Learner object that creates a classifier from an image list. More...

#include <cvb/minos/classifier_factory.hpp>

Public Member Functions
std::unique_ptr< Classifier >	Learn (const TrainingSet &trainingSet)
	Learn a new classifier from the trainingSet using the parameters stored in the properties of this object. More...

int	IndifferenceRadius () const noexcept
	Get the minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image. More...

void	SetIndifferenceRadius (int indifferenceRadius)
	Set the minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image. More...

double	NegativeDensity () const noexcept
	Get the scan density with which to extract counter samples from the training set images. More...

void	SetNegativeDensity (double negativeDensity)
	Set the scan density with which to extract counter samples from the training set images. Higher densities will lead to a classifier that is potentially more robust versus false positive detections, but will also result in increased learning times. More...

int	ContrastTrigger () const noexcept
	Get the minimum contrast a Minos feature must achieve before it is eligible to become part of the classifier. More...

void	SetContrastTrigger (int contrastTrigger)
	Set the minimum contrast a Minos feature must achieve before it is eligible to become part of the classifier. Lower values will lead to a classifier that is more sensitive in low-contrast situation but might also increase the number of false positive results that need to be filtered out e.g. by means of their quality measure. More...

int	EnsembleSize () const noexcept
	Get the maximum size of the Ensembles of similar instance images to be used for pair feature calculation. More...

void	SetEnsembleSize (int ensembleSize)
	Set the maximum size of the Ensembles of similar instance images to be used for pair feature calculation. Valid ensemble sizes must be greater than 0, the default value is 15. More...

int	Polydromy () const noexcept
	Get the polydromy value controlling the complexity of the feature search tree in the classifier. More...

void	SetPolydromy (int polydromy)
	Set the polydromy value controlling the complexity of the feature search tree in the classifier. Valid polydromy values must be greater than 0, the default value is 2. More...

int	MinPairFeatures () const noexcept
	Get the minimum number of features to extract for each model when building a classifier. More...

void	SetMinPairFeatures (int minPairFeatures)
	Set the minimum number of features to extract for each model when building a classifier. More...

Static Public Member Functions
static int	IndifferenceRadiusDefault () noexcept
	Default value for the IndifferenceRadius. More...

static double	NegativeDensityDefault () noexcept
	Default value for the NegativeDensity. More...

static int	ContrastTriggerDefault () noexcept
	Default value for the ContrastTrigger. More...

static int	EnsembleSizeDefault () noexcept
	Default value for the EnsembleSize. More...

static int	PolydromyDefault () noexcept
	Default value for the Polydromy. More...

static int	MinPairFeaturesDefault () noexcept
	Default value for the MinPairFeatures. More...

Detailed Description

Learner object that creates a classifier from an image list.

Member Function Documentation

◆ ContrastTrigger()

int ContrastTrigger ( ) const

inlinenoexcept

Get the minimum contrast a Minos feature must achieve before it is eligible to become part of the classifier.

Returns: The contrast trigger value.

Exceptions

Does	not throw any exception.

◆ ContrastTriggerDefault()

static int ContrastTriggerDefault ( )

inlinestaticnoexcept

Default value for the ContrastTrigger.

Returns: Default constrast trigger value.

Exceptions

Does	not throw any exception.

◆ EnsembleSize()

int EnsembleSize ( ) const

inlinenoexcept

Get the maximum size of the Ensembles of similar instance images to be used for pair feature calculation.

Returns: The ensemble size value.

Exceptions

Does	not throw any exception.

◆ EnsembleSizeDefault()

static int EnsembleSizeDefault ( )

inlinestaticnoexcept

Default value for the EnsembleSize.

Returns: Default ensemble size value.

Exceptions

Does	not throw any exception.

◆ IndifferenceRadius()

int IndifferenceRadius ( ) const

inlinenoexcept

Get the minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image.

Returns: The indifference radius value.

Exceptions

Does	not throw any exception.

◆ IndifferenceRadiusDefault()

static int IndifferenceRadiusDefault ( )

inlinestaticnoexcept

Default value for the IndifferenceRadius.

Returns: Default indeference radius value.

Exceptions

Does	not throw any exception.

◆ Learn()

std::unique_ptr< Classifier > Learn ( const TrainingSet & trainingSet )

inline

Learn a new classifier from the trainingSet using the parameters stored in the properties of this object.

Parameters

[in] trainingSet Training set from which to learn.

Returns: Newly learned classifier object.

Exceptions

Any	exception derived from std::exception including CvbException.

◆ MinPairFeatures()

int MinPairFeatures ( ) const

inlinenoexcept

Get the minimum number of features to extract for each model when building a classifier.

Returns: The minimum feature count.

Exceptions

Does	not throw any exception.

◆ MinPairFeaturesDefault()

static int MinPairFeaturesDefault ( )

inlinestaticnoexcept

Default value for the MinPairFeatures.

Returns: Default min pair features value.

Exceptions

Does	not throw any exception.

◆ NegativeDensity()

double NegativeDensity ( ) const

inlinenoexcept

Get the scan density with which to extract counter samples from the training set images.

Returns: The negative density value.

Exceptions

Does	not throw any exception.

◆ NegativeDensityDefault()

static double NegativeDensityDefault ( )

inlinestaticnoexcept

Default value for the NegativeDensity.

Returns: Default negative density value.

Exceptions

Does	not throw any exception.

◆ Polydromy()

int Polydromy ( ) const

inlinenoexcept

Get the polydromy value controlling the complexity of the feature search tree in the classifier.

Returns: The polydromy value.

Exceptions

Does	not throw any exception.

◆ PolydromyDefault()

static int PolydromyDefault ( )

inlinestaticnoexcept

Default value for the Polydromy.

Returns: Default polydromy value.

Exceptions

Does	not throw any exception.

◆ SetContrastTrigger()

void SetContrastTrigger ( int contrastTrigger )

inline

Set the minimum contrast a Minos feature must achieve before it is eligible to become part of the classifier. Lower values will lead to a classifier that is more sensitive in low-contrast situation but might also increase the number of false positive results that need to be filtered out e.g. by means of their quality measure.

Parameters

[in] contrastTrigger The contrast trigger value.

Exceptions

Any	exception derived from std::exception including CvbException.

◆ SetEnsembleSize()

void SetEnsembleSize ( int ensembleSize )

inline

Set the maximum size of the Ensembles of similar instance images to be used for pair feature calculation. Valid ensemble sizes must be greater than 0, the default value is 15.

Parameters

[in] ensembleSize The ensemble size value.

Exceptions

Any	exception derived from std::exception including CvbException.

◆ SetIndifferenceRadius()

void SetIndifferenceRadius ( int indifferenceRadius )

inline

Set the minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image.

Parameters

[in] indifferenceRadius The indifference radius value.

Exceptions

Any	exception derived from std::exception including CvbException.

◆ SetMinPairFeatures()

void SetMinPairFeatures ( int minPairFeatures )

inline

Set the minimum number of features to extract for each model when building a classifier.

Parameters

[in] minPairFeatures The minimum feature count.

Exceptions

Any	exception derived from std::exception including CvbException.

◆ SetNegativeDensity()

void SetNegativeDensity ( double negativeDensity )

inline

Set the scan density with which to extract counter samples from the training set images. Higher densities will lead to a classifier that is potentially more robust versus false positive detections, but will also result in increased learning times.

Parameters

[in] negativeDensity The negative density value.

Exceptions

Any	exception derived from std::exception including CvbException.

◆ SetPolydromy()

void SetPolydromy ( int polydromy )

inline

Set the polydromy value controlling the complexity of the feature search tree in the classifier. Valid polydromy values must be greater than 0, the default value is 2.

Parameters

[in] polydromy The polydromy value.

Exceptions

Any	exception derived from std::exception including CvbException.

Public Member Functions

Static Public Member Functions

Detailed Description

Member Function Documentation

◆ ContrastTrigger()

◆ ContrastTriggerDefault()

◆ EnsembleSize()

◆ EnsembleSizeDefault()

◆ IndifferenceRadius()

◆ IndifferenceRadiusDefault()

◆ Learn()

◆ MinPairFeatures()

◆ MinPairFeaturesDefault()

◆ NegativeDensity()

◆ NegativeDensityDefault()

◆ Polydromy()

◆ PolydromyDefault()

◆ SetContrastTrigger()

◆ SetEnsembleSize()

◆ SetIndifferenceRadius()

◆ SetMinPairFeatures()

◆ SetNegativeDensity()

◆ SetPolydromy()