Base abstract class for edge potentials training. More...

#include <TrainEdge.h>

Inheritance diagram for DirectGraphicalModels::CTrainEdge:

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Collaboration diagram for DirectGraphicalModels::CTrainEdge:

Public Member Functions
	CTrainEdge (byte nStates, word nFeatures)
	Constructor. More...

virtual	~CTrainEdge (void)=default

virtual void	addFeatureVecs (const Mat &featureVector1, byte gt1, const Mat &featureVector2, byte gt2)=0
	Adds a pair of feature vectors. More...

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

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	reset (void)=0
	Resets class variables. More...

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...

Static Public Member Functions
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 Member Functions
virtual Mat	calculateEdgePotentials (const Mat &featureVector1, const Mat &featureVector2, const vec_float_t &vParams) const =0
	Calculates the edge potential, based on the feature vectors. More...

Protected Member Functions inherited from DirectGraphicalModels::CBaseRandomModel
virtual void	saveFile (FILE *pFile) const =0
	Saves the random model into the file. More...

virtual void	loadFile (FILE *pFile)=0
	Loads the random model from the file. 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...

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

Detailed Description

Base abstract class for edge potentials training.

Refer to the Demo Train for the application and common usage example

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 24 of file TrainEdge.h.

Constructor & Destructor Documentation

◆ CTrainEdge()

DirectGraphicalModels::CTrainEdge::CTrainEdge	(	byte	nStates,
		word	nFeatures
	)

inline

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features

Definition at line 32 of file TrainEdge.h.

◆ ~CTrainEdge()

virtual DirectGraphicalModels::CTrainEdge::~CTrainEdge ( void )

virtualdefault

Member Function Documentation

◆ addFeatureVecs()

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

pure 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

Implemented in DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >, DirectGraphicalModels::CTrainEdgePotts, and DirectGraphicalModels::CTrainEdgePrior.

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◆ calculateEdgePotentials()

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

protectedpure virtual

Calculates the edge potential, based on the feature vectors.

This function calculates the potentials of an edge, described with two samples featureVector1 and featureVector2, correspondig to the both nodes defining that edge. The resulting potentials of the two nodes being in each possible state (belonging to each possible class) at the same time, are united in the edge potential matrix:

\[edgePot[nStates][nStates] = f(\textbf{f}_1[nFeatures],\textbf{f}_2[nFeatures]).\]

Functions \( f \) must be implemented in derived classes.

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. Please refere to the concrete model implementation of the calculateEdgePotentials() function for more details

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

Implemented in DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >, DirectGraphicalModels::CTrainEdgePottsCS, DirectGraphicalModels::CTrainEdgePrior, and DirectGraphicalModels::CTrainEdgePotts.

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◆ create()

std::shared_ptr< CTrainEdge > DirectGraphicalModels::CTrainEdge::create	(	byte	edgeRandomModel,
		byte	nStates,
		word	nFeatures
	)

static

Factory method returning edge trainer object.

Note: The resulting edge trainer object is created with default parameters

Parameters

edgeRandomModel	Type of desired random model (Ref. EdgeRandomModel)
nStates	Number of states (classes)
nFeatures	Number of features

Returns: Tne pointer to the concrete implementation of the edge trainer class

Definition at line 16 of file TrainEdge.cpp.

◆ getDefaultEdgePotentials() [1/2]

static Mat DirectGraphicalModels::CTrainEdge::getDefaultEdgePotentials	(	float	val,
		byte	nStates
	)

inlinestatic

Returns the data-independent edge potentials.

This function returns matrix with diagonal elements equal to the argument val, all the other elements are 1's, what imitates the Potts model.

\[edgePot[nStates][nStates] = \begin{bmatrix} val & 1 & \cdots & 1 \\ 1 & val & \cdots & 1 \\ \vdots & \vdots & \ddots & \vdots \\ 1 & 1 & \cdots & val \end{bmatrix} \]

Parameters

val	The diagonal element of the matrix
nStates	Number of states (classes)

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

Definition at line 74 of file TrainEdge.h.

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◆ getDefaultEdgePotentials() [2/2]

Mat DirectGraphicalModels::CTrainEdge::getDefaultEdgePotentials ( const vec_float_t & values )

static

Returns the data-independent edge potentials.

This function returns matrix with diagonal elements specified by argument array values, all the other elements are 1's, what imitates the Potts model.

\[edgePot[nStates][nStates] = \begin{bmatrix} values_1 & 1 & \cdots & 1 \\ 1 & values_2 & \cdots & 1 \\ \vdots & \vdots & \ddots & \vdots \\ 1 & 1 & \cdots & values_{nStates} \end{bmatrix} \]

Parameters

values The array of diagonal elements of the matrix. In must include nStates values, specifying smoothness strength for each state (class) individually.

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

Definition at line 47 of file TrainEdge.cpp.

◆ getEdgePotentials()

Mat DirectGraphicalModels::CTrainEdge::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.

This function calls calculateEdgePotentials() function, which should be implemented in derived classes. After that, the resulting edge potential is powered by parameter weight.

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. Please refer to the concrete model implementation of the calculateEdgePotentials() function for more details
weight	The weighting parameter

Returns: Edge potentials on success: Mat(size: nStates x nStates; type: CV_32FC1)

Definition at line 29 of file TrainEdge.cpp.

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◆ train()

virtual void DirectGraphicalModels::CTrainEdge::train ( bool doClean = false )

inlinevirtual

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

Implements DirectGraphicalModels::ITrain.

Reimplemented in DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >, and DirectGraphicalModels::CTrainEdgePrior.

Definition at line 54 of file TrainEdge.h.

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

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

Public Member Functions

Static Public Member Functions

Protected Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ CTrainEdge()

◆ ~CTrainEdge()

Member Function Documentation

◆ addFeatureVecs()

◆ calculateEdgePotentials()

◆ create()

◆ getDefaultEdgePotentials() [1/2]

◆ getDefaultEdgePotentials() [2/2]

◆ getEdgePotentials()

◆ train()