Concatenated edge training class. More...

#include <TrainEdgeConcat.h>

Inheritance diagram for DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >:

Collaboration diagram for DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >:

[legend]

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

template<class TrainerParams >
	CTrainEdgeConcat (byte nStates, word nFeatures, TrainerParams params)
	Constructor. More...

virtual	~CTrainEdgeConcat (void)

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

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

void	load (const std::string &path, const std::string &name=std::string(), short idx=-1)
	Loads the training data. 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::CTrainEdge
	CTrainEdge (byte nStates, word nFeatures)
	Constructor. More...

virtual	~CTrainEdge (void)

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

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

Mat	getEdgePotentials (const Mat &featureVector1, const Mat &featureVector2, float *params, size_t params_len, 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)

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

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

virtual	~CBaseRandomModel (void)

byte	getNumStates (void) const
	Returns number of features. 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, float *params, size_t params_len) const
	Returns the data-dependent edge potentials. 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...

Protected Attributes
CPriorNode *	m_pPrior
	Node prior poobability More...

CTrainNode *	m_pTrainer
	Node trainer More...

CFeaturesConcatenator *	m_pConcatenator
	Feature concatenator. More...

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

template<class Trainer, class Concatenator>
class DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >

Concatenated edge training class.

This class in order to estimate edge potentials, uses an unary potential trainer, which is learned for $nStates^2$ states (classes). Both edge feature vectors are concatenated in one feature vector, which in its turn, is used for the unary potential trainer. The feature concatenation is performed by the CFeaturesConcatenator class.

Template Parameters

Trainer	The nested node potential trainer, derived from the CTrainNode class
Concatenator	The feature concatenator, derived from the CFeaturesConcatenator class

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 TrainEdgeConcat.h.

Constructor & Destructor Documentation

template<class Trainer , class Concatenator >

DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::CTrainEdgeConcat	(	byte	nStates,
		word	nFeatures
	)

inline

Constructor.

Parameters

nStates	Number of states (classes) Must be less then 16
nFeatures	Number of features

Definition at line 33 of file TrainEdgeConcat.h.

Here is the call graph for this function:

template<class Trainer , class Concatenator >

template<class TrainerParams >

DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::CTrainEdgeConcat	(	byte	nStates,
		word	nFeatures,
		TrainerParams	params
	)

inline

Constructor.

Template Parameters

TrainerParams Type of the parameters to the nested node potential trainer

Parameters

nStates	Number of states (classes) Must be less then 16
nFeatures	Number of features
params	Parameters of the nested node potential trainer

Definition at line 51 of file TrainEdgeConcat.h.

Here is the call graph for this function:

template<class Trainer , class Concatenator >

virtual DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::~CTrainEdgeConcat ( void )

inlinevirtual

Definition at line 63 of file TrainEdgeConcat.h.

Member Function Documentation

template<class Trainer , class Concatenator >

virtual void DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::addFeatureVecs	(	const Mat &	featureVector1,
		byte	gt1,
		const Mat &	featureVector2,
		byte	gt2
	)

inlinevirtual

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

Implements DirectGraphicalModels::CTrainEdge.

Definition at line 74 of file TrainEdgeConcat.h.

Here is the call graph for this function:

template<class Trainer , class Concatenator >

virtual Mat DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::calculateEdgePotentials	(	const Mat &	featureVector1,
		const Mat &	featureVector2,
		float *	params,
		size_t	params_len
	)		const

inlineprotectedvirtual

Returns the data-dependent edge potentials.

This function returns edge potential matrix, which elements are obrained from the unary potential vector:

\[ edgePot[nStates][nStates] = nodePot[nStates^2] = f(concat(\textbf{f}_1[nFeatures],\textbf{f}_2[nFeatures])). \]

The resulting edge potential matrix is normalized such that its largest element is equal to paramter $\theta$, and then regularized as follows:

\[ edgePot_{s,s} = \left\{\begin{array}{ll} 1 &\mbox{ if $edgePot_{s,s}<1$} \\ edgePot_{s,s} &\mbox{ otherwise}\end{array} \right.\;\;\;\forall s\in\mathbb{S}.\]

Parameters

featureVector1	Multi-dimensinal point $\textbf{f}_1$: Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to the first node of the edge
featureVector2	Multi-dimensinal point $\textbf{f}_2$: Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to the second node of the edge
params	Array of control parameters $\vec{\theta}$, which must consist from one parameter, specifying the largest value in the resulting edge potential.
params_len	The length of the params parameter. It must be equal to 1.

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

Implements DirectGraphicalModels::CTrainEdge.

Definition at line 100 of file TrainEdgeConcat.h.

Here is the call graph for this function:

template<class Trainer , class Concatenator >

void DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::load	(	const std::string &	path,
		const std::string &	name = `std::string()`,
		short	idx = `-1`
	)

inlinevirtual

Loads the training data.

Allows to re-use the class. Loads data to the file: "<path><name>_<idx>.dat".

Parameters

path	Path to the folder, containing the data file.
name	Name of data file. If empty, will be generated automatically from the class name.
idx	Index of the data file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 72 of file TrainEdgeConcat.h.

Here is the call graph for this function:

template<class Trainer , class Concatenator >

virtual void DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::loadFile ( FILE * pFile )

inlineprotectedvirtual

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 87 of file TrainEdgeConcat.h.

template<class Trainer , class Concatenator >

virtual void DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::reset ( void )

inlinevirtual

Resets class variables.

Allows to re-use the class.

Implements DirectGraphicalModels::CBaseRandomModel.

Definition at line 70 of file TrainEdgeConcat.h.

Here is the call graph for this function:

template<class Trainer , class Concatenator >

void DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::save	(	const std::string &	path,
		const std::string &	name = `std::string()`,
		short	idx = `-1`
	)		const

inlinevirtual

Saves the training data.

Allows to re-use the class. Stores data to the file: "<path><name>_<idx>.dat".

Parameters

path	Path to the destination folder.
name	Name of data file. If empty, will be generated automatically from the class name.
idx	Index of the destination file. Negative value means no index.

Reimplemented from DirectGraphicalModels::CBaseRandomModel.

Definition at line 71 of file TrainEdgeConcat.h.

Here is the call graph for this function:

template<class Trainer , class Concatenator >

virtual void DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::saveFile ( FILE * pFile ) const

inlineprotectedvirtual

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 86 of file TrainEdgeConcat.h.

template<class Trainer , class Concatenator >

virtual void DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::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

Reimplemented from DirectGraphicalModels::CTrainEdge.

Definition at line 82 of file TrainEdgeConcat.h.

Here is the call graph for this function:

Member Data Documentation

template<class Trainer , class Concatenator >

CFeaturesConcatenator* DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::m_pConcatenator

protected

Feature concatenator.

Definition at line 126 of file TrainEdgeConcat.h.

template<class Trainer , class Concatenator >

CPriorNode* DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::m_pPrior

protected

Node prior poobability

Definition at line 124 of file TrainEdgeConcat.h.

template<class Trainer , class Concatenator >

CTrainNode* DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >::m_pTrainer

protected

Node trainer

Definition at line 125 of file TrainEdgeConcat.h.

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

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

featureVector1	Multi-dimensinal point \(\textbf{f}_1\): Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to the first node of the edge
featureVector2	Multi-dimensinal point \(\textbf{f}_2\): Mat(size: nFeatures x 1; type: CV_8UC1), corresponding to the second node of the edge
params	Array of control parameters \(\vec{\theta}\), which must consist from one parameter, specifying the largest value in the resulting edge potential.
params_len	The length of the params parameter. It must be equal to 1.

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

template<class Trainer, class Concatenator> class DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<class Trainer, class Concatenator>
class DirectGraphicalModels::CTrainEdgeConcat< Trainer, Concatenator >