CVB.Net 14.0
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. More...
 

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. More...
 
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. More...
 
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< LearnProgressEventArgsLearnProgress
 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
trainingSettraining set from which to learn
Returns
newly learned classifier object
Exceptions
ArgumentNullExceptionwhen 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
ArgumentOutOfRangeExceptionwhen 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
ArgumentOutOfRangeExceptionwhen trying to set an invalid scan density (valid values are within the range [0...1]