Gaussian Mixture Model training class. More...

#include <TrainNodeGMM.h>

Inheritance diagram for DirectGraphicalModels::CTrainNodeGMM:

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Collaboration diagram for DirectGraphicalModels::CTrainNodeGMM:

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)

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

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

Mat	getNodePotentials (const Mat &featureVector, float weight=1.0f) 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)

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 features. 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...

Additional Inherited Members
Protected Attributes inherited from DirectGraphicalModels::ITrain
word	m_nFeatures
	The number of features (length of the feature vector) 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 realizes 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 38 of file TrainNodeGMM.h.

Constructor & Destructor Documentation

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)

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)

virtual DirectGraphicalModels::CTrainNodeGMM::~CTrainNodeGMM ( void )

virtual

Member Function Documentation

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.

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 CNDGauss

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.

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.

void DirectGraphicalModels::CTrainNodeGMM::reset ( void )

virtual

Resets class variables.

Allows to re-use the class.

Implements DirectGraphicalModels::CBaseRandomModel.

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.

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.

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

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

Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation