Potts edge training class. More...

#include <TrainEdgePotts.h>

Inheritance diagram for DirectGraphicalModels::CTrainEdgePotts:

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Collaboration diagram for DirectGraphicalModels::CTrainEdgePotts:

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

virtual	~CTrainEdgePotts (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...

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

virtual	~CTrainEdge (void)=default

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	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
	Returns the data-independent 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...

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

Potts edge training class.

This class implements the Potts model for edge (pairwise) potentials, which is both training- and test-data-independent, and thus may be applied without edge training procedure, i.e. functions reset(), save(), load(), addFeatureVecs() are unnecessary here.

This class may be used in case when the training- and test-data for edges is not available.

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 19 of file TrainEdgePotts.h.

Constructor & Destructor Documentation

◆ CTrainEdgePotts()

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

inline

Constructor.

Parameters

nStates	Number of states (classes)
nFeatures	Number of features

Definition at line 27 of file TrainEdgePotts.h.

◆ ~CTrainEdgePotts()

virtual DirectGraphicalModels::CTrainEdgePotts::~CTrainEdgePotts ( void )

inlinevirtual

Definition at line 28 of file TrainEdgePotts.h.

Member Function Documentation

◆ addFeatureVecs()

virtual void DirectGraphicalModels::CTrainEdgePotts::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.

Reimplemented in DirectGraphicalModels::CTrainEdgePrior.

Definition at line 32 of file TrainEdgePotts.h.

◆ calculateEdgePotentials()

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

protectedvirtual

Returns the data-independent edge potentials.

This function returns matrix with diagonal elements equal to parameter \(\vec{\theta}\) provided through argument params; all the other elements are 1's - this imitates the Potts model.

\[edgePot[nStates][nStates] = \begin{bmatrix} \theta_1 & 1 & \cdots & 1 \\ 1 & \theta_2 & \cdots & 1 \\ \vdots & \vdots & \ddots & \vdots \\ 1 & 1 & \cdots & \theta_{nStates} \end{bmatrix} \]

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 It is not used in the Potts model, thus may be empty Mat()
featureVector2	Multi-dimensinal point \(\textbf{f}_2\): Mat(size: nFeatures x 1; type: CV_{XX}C1), corresponding to the second node of the edge It is not used in the Potts model, thus may be empty Mat()
vParams	Array of control parameters \(\vec{\theta}\), which 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.