Contrast-Sensitive Potts training with edge prior probability class. More...

#include <TrainEdgePrior.h>

Inheritance diagram for DirectGraphicalModels::CTrainEdgePrior:

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Collaboration diagram for DirectGraphicalModels::CTrainEdgePrior:

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Public Member Functions
	CTrainEdgePrior (byte nStates, word nFeatures, ePotPenalApproach penApproach=eP_APP_PEN_EXP, ePotNormApproach normApproach=eP_APP_NORM_SYMMETRIC)
	Constructor. More...

virtual	~CTrainEdgePrior (void)

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

virtual void	addFeatureVecs (const Mat &featureVector1, byte gt1, const Mat &featureVector2, byte gt2)
	Adds a pair of feature vectors. More...

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

Public Member Functions inherited from DirectGraphicalModels::CTrainEdgePottsCS
	CTrainEdgePottsCS (byte nStates, word nFeatures, ePotPenalApproach penApproach=eP_APP_PEN_EXP)
	Constructor. More...

virtual	~CTrainEdgePottsCS (void)

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

virtual	~CTrainEdgePotts (void)

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

virtual	~CTrainEdge (void)=default

Mat	getEdgePotentials (const Mat &featureVector1, const Mat &featureVector2, const vec_float_t &vParams, float weight=1.0f) const
	Returns the edge potential, based on the feature vectors. 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
virtual void	saveFile (FILE *pFile) const
	Saves the random model into the file. More...

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

virtual Mat	calculateEdgePotentials (const Mat &featureVector1, const Mat &featureVector2, const vec_float_t &vParams) const
	Calculates the edge potential, based on the feature vectors. 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 Member Functions
void	loadPriorMatrix (void)

Private Member Functions inherited from DirectGraphicalModels::CPriorEdge
	CPriorEdge (byte nStates, ePotNormApproach normApp=eP_APP_NORM_SYMMETRIC)
	Constructor. More...

	~CPriorEdge (void)

void	addEdgeGroundTruth (byte gt1, byte gt2)
	Adds the groud-truth value to the co-occurance histogram matrix. More...

Mat	calculatePrior (void) const
	Returns the prior edge probability. More...

Private Member Functions inherited from DirectGraphicalModels::CPrior
	CPrior (byte nStates, RandomModelType type)
	Constructor. More...

	~CPrior (void)

Mat	getPrior (float weight=1.0f) const
	Returns the prior probabilies. More...

Private 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...

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
Mat	m_prior

Private Attributes inherited from DirectGraphicalModels::CPrior
Mat	m_histogramPrior
	The class cooccurance histogram. More...

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

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

static Mat	getDefaultEdgePotentials (float val, byte nStates)
	Returns the data-independent edge potentials. More...

static Mat	getDefaultEdgePotentials (const vec_float_t &values)
	Returns the data-independent edge potentials. More...

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

Detailed Description

Contrast-Sensitive Potts training with edge prior probability class.

This class improves the CTrainEdgePottsCS class by considering the prior probability of an edge to connect two nodes with concrete states (classes).

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 17 of file TrainEdgePrior.h.

Constructor & Destructor Documentation

◆ CTrainEdgePrior()

DirectGraphicalModels::CTrainEdgePrior::CTrainEdgePrior	(	byte	nStates,
		word	nFeatures,
		ePotPenalApproach	penApproach = `eP_APP_PEN_EXP`,
		ePotNormApproach	normApproach = `eP_APP_NORM_SYMMETRIC`
	)

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features
penApproach	Flag specifying the penalization approach for the edge potential matrix (Ref. ePotPenalApproach)
normApproach	Flag specifying the co-occurance histogram matrix normalization approach (Ref. ePotNormApproach)

Definition at line 7 of file TrainEdgePrior.cpp.

◆ ~CTrainEdgePrior()

DirectGraphicalModels::CTrainEdgePrior::~CTrainEdgePrior ( void )

virtual

Definition at line 15 of file TrainEdgePrior.cpp.

Member Function Documentation

◆ addFeatureVecs()

void DirectGraphicalModels::CTrainEdgePrior::addFeatureVecs	(	const Mat &	featureVector1,
		byte	gt1,
		const Mat &	featureVector2,
		byte	gt2
	)

virtual

Adds a pair of feature vectors.

Used to add featureVector1 and featureVector2, corresponding to the ground-truth states (classes) gt1 and gt2 for training. Here the couple {featureVector1, gt1} corresponds to the first node of the edge, and the couple {featureVector2, gt2} - to the second node.

Parameters

featureVector1	Multi-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to the first node of the edge.
gt1	The ground-truth state (class) of the first node of the edge, given by featureVector1
featureVector2	Multi-dimensinal point: Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to the second node of the edge.
gt2	The ground-truth state (class) of the second node of the edge, given by featureVector2

Reimplemented from DirectGraphicalModels::CTrainEdgePotts.

Definition at line 27 of file TrainEdgePrior.cpp.

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◆ calculateEdgePotentials()

Mat DirectGraphicalModels::CTrainEdgePrior::calculateEdgePotentials	(	const Mat &	featureVector1,
		const Mat &	featureVector2,
		const vec_float_t &	vParams
	)		const

protectedvirtual

Calculates the edge potential, based on the feature vectors.

This function returns matrix with diagonal elements equal to parameter \(\vec{\theta}\) provided through argument params penalized with a penalization function \(\mathcal{P}\), which depends on the difference between arguments featureVector1 and featureVector2 and multiplied with the corresponding edge prior probability; all the other elements are the edge prior probability matrix values:

\[edgePot[nStates][nStates] = \begin{bmatrix} \theta_1\cdot\mathcal{P}\cdot p_{1,1} & p_{1,2} & \cdots & p_{1,n} \\ p_{2,1} & \theta_2\cdot\mathcal{P}\cdot p_{2,2} & \cdots & p_{2,n} \\ \vdots & \vdots & \ddots & \vdots \\ p_{n,1} & p_{n,2} & \cdots & \theta_n\cdot\mathcal{P}\cdot p_{n,n} \end{bmatrix}, \]

where \(p_{i,j}\) is the normalized according to ePotNormApproach prior probability of edge to connect nodes with states (classes) i and j, \(\mathcal{P}\equiv\mathcal{P}(d;\,\lambda)\) is the penalization function, where \(d = ||\textbf{f}_1 - \textbf{f}_2||_2\) is the Euclidean distance between feature vectors,
and \(\lambda\) is a parameter provided through argument params. For more details on penalization fuction, please refere to ePotPenalApproach.

Parameters

featureVector1	Multi-dimensinal point \(\textbf{f}_1\): Mat(size: nFeatures x 1; type: CV_{XX}C1), corresponding to the first node of the edge
featureVector2	Multi-dimensinal point \(\textbf{f}_2\): Mat(size: nFeatures x 1; type: CV_{XX}C1), corresponding to the second node of the edge
vParams	Array of control parameters \(\{\vec{\theta},\lambda\} \). \(\vec{\theta}\) may consist either from one parameter (in this case all the diagonal elemets will be the same), or from nStates parameters, specifying smoothness strength for each state (class) individually; \(\lambda\) consists from one parameter.

Returns: The edge potential matrix: Mat(size: nStates x nStates; type: CV_32FC1)

Reimplemented from DirectGraphicalModels::CTrainEdgePottsCS.

Definition at line 48 of file TrainEdgePrior.cpp.

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◆ loadFile()

void DirectGraphicalModels::CTrainEdgePrior::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.

Reimplemented from DirectGraphicalModels::CPrior.

Definition at line 42 of file TrainEdgePrior.cpp.

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◆ loadPriorMatrix()

void DirectGraphicalModels::CTrainEdgePrior::loadPriorMatrix ( void )

inlineprivate

Definition at line 55 of file TrainEdgePrior.cpp.

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◆ reset()

void DirectGraphicalModels::CTrainEdgePrior::reset ( void )

virtual

Resets class variables.

Allows to re-use the class.

Reimplemented from DirectGraphicalModels::CPrior.

Definition at line 21 of file TrainEdgePrior.cpp.

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◆ saveFile()

void DirectGraphicalModels::CTrainEdgePrior::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.

Reimplemented from DirectGraphicalModels::CPrior.

Definition at line 37 of file TrainEdgePrior.cpp.

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◆ train()

void DirectGraphicalModels::CTrainEdgePrior::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::CTrainEdge.

Definition at line 32 of file TrainEdgePrior.cpp.

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Member Data Documentation

◆ m_prior

Mat DirectGraphicalModels::CTrainEdgePrior::m_prior

private

Definition at line 63 of file TrainEdgePrior.h.

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

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

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ CTrainEdgePrior()

◆ ~CTrainEdgePrior()

Member Function Documentation

◆ addFeatureVecs()

◆ calculateEdgePotentials()

◆ loadFile()

◆ loadPriorMatrix()

◆ reset()

◆ saveFile()

◆ train()

Member Data Documentation

◆ m_prior