Gaussian Mixture Model training class. More...

#include <TrainNodeGMM.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeGMM:

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Collaboration diagram for DirectGraphicalModels::CTrainNodeGMM:

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Public Member Functions
	CTrainNodeGMM (byte nStates, word nFeatures, TrainNodeGMMParams params=TRAIN_NODE_GMM_PARAMS_DEFAULT)
	Constructor. More...

	CTrainNodeGMM (byte nStates, word nFeatures, byte maxGausses)
	Constructor. More...

virtual	~CTrainNodeGMM (void)

void	reset (void)
	Resets class variables. More...

void	addFeatureVec (const Mat &featureVector, byte gt)
	Adds new feature vector. More...

void	train (bool doClean=false)
	Random model training. More...

Public Member Functions inherited from DirectGraphicalModels::CTrainNode
	CTrainNode (byte nStates, word nFeatures)
	Constructor. More...

virtual	~CTrainNode (void)=default

void	addFeatureVecs (const Mat &featureVectors, const Mat &gt)
	Adds a block of new feature vectors. More...

void	addFeatureVecs (const vec_mat_t &featureVectors, const Mat &gt)
	Adds a block of new feature vectors. More...

Mat	getNodePotentials (const Mat &featureVectors, const Mat &weights=Mat(), float Z=0.0f) const
	Returns a block of node potentials, based on the block of feature vector. More...

Mat	getNodePotentials (const vec_mat_t &featureVectors, const Mat &weights=Mat(), float Z=0.0f) const
	Returns a block of node potentials, based on the block of feature vector. More...

Mat	getNodePotentials (const Mat &featureVector, float weight, float Z=0.0f) const
	Returns the node potential, based on the feature vector. More...

Public Member Functions inherited from DirectGraphicalModels::ITrain
	ITrain (byte nStates, word nFeatures)
	Constructor. More...

virtual	~ITrain (void)=default

word	getNumFeatures (void) const
	Returns number of features. More...

Public Member Functions inherited from DirectGraphicalModels::CBaseRandomModel
	CBaseRandomModel (byte nStates)
	Constructor. More...

virtual	~CBaseRandomModel (void)

virtual void	save (const std::string &path, const std::string &name=std::string(), short idx=-1) const
	Saves the training data. More...

virtual void	load (const std::string &path, const std::string &name=std::string(), short idx=-1)
	Loads the training data. More...

byte	getNumStates (void) const
	Returns number of states (classes) More...

Protected Member Functions
void	saveFile (FILE *pFile) const
	Saves the random model into the file. More...

void	loadFile (FILE *pFile)
	Loads the random model from the file. More...

void	calculateNodePotentials (const Mat &featureVector, Mat &potential, Mat &mask) const
	Calculates the node potential, based on the feature vector. More...

Protected Member Functions inherited from DirectGraphicalModels::CBaseRandomModel
std::string	generateFileName (const std::string &path, const std::string &name, short idx) const
	Generates name of the data file for storing random model parameters. More...

Private Attributes
TrainNodeGMMParams	m_params

std::vector< GaussianMixture >	m_vGaussianMixtures

long double	m_minAlpha = 1

Static Private Attributes
static const size_t	MIN_SAMPLES = 16

static const long double	MAX_COEFFICIENT = 1.0

Additional Inherited Members
Static Public Member Functions inherited from DirectGraphicalModels::CTrainNode
static std::shared_ptr< CTrainNode >	create (byte nodeRandomModel, byte nStates, word nFeatures)
	Factory method returning node trainer object. More...

Protected Attributes inherited from DirectGraphicalModels::CBaseRandomModel
byte	m_nStates
	The number of states (classes) More...

Detailed Description

Gaussian Mixture Model training class.

This class implements the generative training mechanism, based on the idea of approximating the density of multi-dimensional random variables with an additive super-position of multivariate Gaussian distributions. The underlying algorithm is described in the paper Sequential Gaussian Mixture Models for Two-Level Conditional Random Fields

Author: Sergey G. Kosov, serge.nosp@m.y.ko.nosp@m.sov@p.nosp@m.roje.nosp@m.ct-10.nosp@m..de

Definition at line 40 of file TrainNodeGMM.h.

Constructor & Destructor Documentation

◆ CTrainNodeGMM() [1/2]

DirectGraphicalModels::CTrainNodeGMM::CTrainNodeGMM	(	byte	nStates,
		word	nFeatures,
		TrainNodeGMMParams	params = `TRAIN_NODE_GMM_PARAMS_DEFAULT`
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features
params	Gaussian Mixture Model parameters (Ref. TrainNodeGMMParams)

Definition at line 11 of file TrainNodeGMM.cpp.

◆ CTrainNodeGMM() [2/2]

DirectGraphicalModels::CTrainNodeGMM::CTrainNodeGMM	(	byte	nStates,
		word	nFeatures,
		byte	maxGausses
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features
maxGausses	The maximal number of mixture components in the Gaussian Mixture Model per state (class)

Definition at line 23 of file TrainNodeGMM.cpp.

◆ ~CTrainNodeGMM()

DirectGraphicalModels::CTrainNodeGMM::~CTrainNodeGMM ( void )

virtual

Definition at line 35 of file TrainNodeGMM.cpp.

Member Function Documentation

◆ addFeatureVec()

void DirectGraphicalModels::CTrainNodeGMM::addFeatureVec	(	const Mat &	featureVector,
		byte	gt
	)

virtual

Adds new feature vector.

Used to add a featureVector, corresponding to the ground-truth state (class) gt for training

Parameters

featureVector	Multi-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1)
gt	Corresponding ground-truth state (class)

Implements DirectGraphicalModels::CTrainNode.

Definition at line 71 of file TrainNodeGMM.cpp.

◆ calculateNodePotentials()

void DirectGraphicalModels::CTrainNodeGMM::calculateNodePotentials	(	const Mat &	featureVector,
		Mat &	potential,
		Mat &	mask
	)		const

protectedvirtual

Calculates the node potential, based on the feature vector.

This function calculates the potentials of the node, described with the sample featureVector ( \( \textbf{f} \)): \( nodePot_s = \sum^{nGaussians_s}_{i=1}\pi_{i,s}\cdot\mathcal{N}_{i,s}(\textbf{f}), \forall s \in \mathbb{S} \), where \(\mathbb{S}\) is the set of all states (classes) and \(\pi\) is a weighted coefficient. In other words, the indexes: \( s \in [0; nStates) \). Here \( \mathcal{N} \) is a Gaussian function kernel, described in class CKDGauss

Parameters

[in]	featureVector	Multi-dimensinal point \(\textbf{f}\): Mat(size: nFeatures x 1; type: CV_{XX}C1)
[in,out]	potential	Node potentials: Mat(size: nStates x 1; type: CV_32FC1). This parameter should be preinitialized and set to value 0.
[in,out]	mask	Relevant Node potentials: Mat(size: nStates x 1; type: CV_8UC1). This parameter should be preinitialized and set to value 1 (all potentials are relevant).

Implements DirectGraphicalModels::CTrainNode.

Definition at line 267 of file TrainNodeGMM.cpp.

◆ loadFile()

void DirectGraphicalModels::CTrainNodeGMM::loadFile ( FILE * pFile )

protectedvirtual

Loads the random model from the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for reading.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 228 of file TrainNodeGMM.cpp.

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◆ reset()

void DirectGraphicalModels::CTrainNodeGMM::reset ( void )

virtual

Resets class variables.

Allows to re-use the class.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 38 of file TrainNodeGMM.cpp.

◆ saveFile()

void DirectGraphicalModels::CTrainNodeGMM::saveFile ( FILE * pFile ) const

protectedvirtual

Saves the random model into the file.

Allows to re-use the class.

Parameters

pFile Pointer to the file, opened for writing.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 196 of file TrainNodeGMM.cpp.

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◆ train()

void DirectGraphicalModels::CTrainNodeGMM::train ( bool doClean = false )

virtual

Random model training.

Auxilary function for training - some derived classes may use this function inbetween training and classification phases

Note: This function must be called inbetween the training and classification phases

Parameters

doClean Flag indicating if the memory, keeping the trining data should be released after training

Reimplemented from DirectGraphicalModels::CTrainNode.

Definition at line 154 of file TrainNodeGMM.cpp.

Member Data Documentation

◆ m_minAlpha

long double DirectGraphicalModels::CTrainNodeGMM::m_minAlpha = 1

private

Definition at line 88 of file TrainNodeGMM.h.

◆ m_params

TrainNodeGMMParams DirectGraphicalModels::CTrainNodeGMM::m_params

private

Definition at line 86 of file TrainNodeGMM.h.

◆ m_vGaussianMixtures

std::vector<GaussianMixture> DirectGraphicalModels::CTrainNodeGMM::m_vGaussianMixtures

private

Definition at line 87 of file TrainNodeGMM.h.

◆ MAX_COEFFICIENT

const long double DirectGraphicalModels::CTrainNodeGMM::MAX_COEFFICIENT = 1.0

staticprivate

Definition at line 82 of file TrainNodeGMM.h.

◆ MIN_SAMPLES

const size_t DirectGraphicalModels::CTrainNodeGMM::MIN_SAMPLES = 16

staticprivate

Definition at line 81 of file TrainNodeGMM.h.

The documentation for this class was generated from the following files:

D:/Projects/DGM/modules/DGM/TrainNodeGMM.h
D:/Projects/DGM/modules/DGM/TrainNodeGMM.cpp

Public Member Functions

Protected Member Functions

Private Attributes

Static Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ CTrainNodeGMM() [1/2]

◆ CTrainNodeGMM() [2/2]

◆ ~CTrainNodeGMM()

Member Function Documentation

◆ addFeatureVec()

◆ calculateNodePotentials()

◆ loadFile()

◆ reset()

◆ saveFile()

◆ train()

Member Data Documentation

◆ m_minAlpha

◆ m_params

◆ m_vGaussianMixtures

◆ MAX_COEFFICIENT

◆ MIN_SAMPLES