ClassifierFactory Class Reference

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

Public Member Functions
	ClassifierFactory ()
	Construct a learner that has no training set set yet.
Classifier	Learn (TrainingSet trainingSet)
	Learn a new classifier from the trainingSet using the parameters stored in the properties of this object.

Static Public Attributes
const int	IndifferenceRadiusDefault = 6
	Default value for the IndifferenceRadius property.
const double	NegativesDensityDefault = 1.0
	Default value for the NegativesDensity property.
const int	ContrastTriggerDefault = 8
	Default value for the ContrastTrigger property.
const int	MinPairFeaturesDefault = 20
	Default value for the MinPairFeatures property.

Properties
int	IndifferenceRadius [get, set]
	Minimum distance to be assumed between a (labeled) positive sample and a counter sample in a training set image.
double	NegativesDensity [get, set]
	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.
int	ContrastTrigger [get, set]
	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.
int	MinPairFeatures [get, set]
	Minimum number of features to extract for each model when building a classifier.

Events
EventHandler< LearnProgressEventArgs >	LearnProgress
	Event that will inform about the progress of an ongoing learn operation.

Detailed Description

Learner object that creates a classifier from an image list.

Member Function Documentation

Learn()

Classifier Learn

(

TrainingSet

trainingSet

)

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

Parameters

trainingSet

training set from which to learn

Returns

newly learned classifier object

Exceptions

ArgumentNullException

when trying to pass a null reference as the trainingSet parameter

Property Documentation

IndifferenceRadius

int IndifferenceRadius

getset

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

Exceptions

ArgumentOutOfRangeException

when trying to set an indifference radius less than 0

NegativesDensity

double NegativesDensity

getset

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.

Exceptions

ArgumentOutOfRangeException

when trying to set an invalid scan density (valid values are within the range [0...1]