#include <itkKdTreeBasedKmeansEstimator.h>

Inheritance diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

Collaboration diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

Detailed Description

template<class TKdTree>
class itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >

fast k-means algorithm implementation using k-d tree structure

It returns k mean vectors that are centroids of k-clusters using pre-generated k-d tree. k-d tree generation is done by the WeightedCentroidKdTreeGenerator. The tree construction needs to be done only once. The resulting k-d tree's non-terminal nodes that have their children nodes have vector sums of measurement vectors that belong to the nodes and the number of measurement vectors in addition to the typical node boundary information and pointers to children nodes. Instead of reassigning every measurement vector to the nearest cluster centroid and recalculating centroid, it maintain a set of cluster centroid candidates and using pruning algorithm that utilizes k-d tree, it updates the means of only relevant candidates at each iterations. It would be faster than traditional implementation of k-means algorithm. However, the k-d tree consumes a large amount of memory. The tree construction time and pruning algorithm's performance are important factors to the whole process's performance. If users want to use k-d tree for some purpose other than k-means estimation, they can use the KdTreeGenerator instead of the WeightedCentroidKdTreeGenerator. It will save the tree construction time and memory usage.

Note: There is a second implementation of k-means algorithm in ITK under the While the Kd tree based implementation is more time efficient, the GLA/LBG based algorithm is more memory efficient.

Recent API changes: The static const macro to get the length of a measurement vector, MeasurementVectorSize has been removed to allow the length of a measurement vector to be specified at run time. It is now obtained from the KdTree set as input. You may query this length using the function GetMeasurementVectorSize().

See Also: ImageKmeansModelEstimator; WeightedCentroidKdTreeGenerator, KdTree

Wiki Examples:

All Examples

Compute kmeans clusters

Definition at line 77 of file itkKdTreeBasedKmeansEstimator.h.

Classes
class	CandidateVector

Public Types
typedef KdTreeNodeType::CentroidType	CentroidType

typedef itksys::hash_map < InstanceIdentifier, unsigned int >	ClusterLabelsType

typedef SmartPointer< const Self >	ConstPointer

typedef DistanceToCentroidMembershipFunctionType::Pointer	DistanceToCentroidMembershipFunctionPointer

typedef DistanceToCentroidMembershipFunction < MeasurementVectorType >	DistanceToCentroidMembershipFunctionType

typedef TKdTree::InstanceIdentifier	InstanceIdentifier

typedef std::vector < ParameterType >	InternalParametersType

typedef TKdTree::KdTreeNodeType	KdTreeNodeType

typedef TKdTree::MeasurementType	MeasurementType

typedef unsigned int	MeasurementVectorSizeType

typedef TKdTree::MeasurementVectorType	MeasurementVectorType

typedef MembershipFunctionType::ConstPointer	MembershipFunctionPointer

typedef MembershipFunctionBase < MeasurementVectorType >	MembershipFunctionType

typedef MembershipFunctionVectorObjectType::Pointer	MembershipFunctionVectorObjectPointer

typedef SimpleDataObjectDecorator < MembershipFunctionVectorType >	MembershipFunctionVectorObjectType

typedef std::vector < MembershipFunctionPointer >	MembershipFunctionVectorType

typedef Array< double >	ParametersType

typedef Array< double >	ParameterType

typedef SmartPointer< Self >	Pointer

typedef TKdTree::SampleType	SampleType

typedef KdTreeBasedKmeansEstimator	Self

typedef Object	Superclass

Public Types inherited from itk::Object
typedef SmartPointer< const Self >	ConstPointer

typedef SmartPointer< Self >	Pointer

typedef Object	Self

typedef LightObject	Superclass

Public Types inherited from itk::LightObject
typedef SmartPointer< const Self >	ConstPointer

typedef SmartPointer< Self >	Pointer

typedef LightObject	Self

Public Member Functions
virtual ::itk::LightObject::Pointer	CreateAnother (void) const

virtual double	GetCentroidPositionChanges () const

virtual double	GetCentroidPositionChangesThreshold () const

virtual int	GetCurrentIteration () const

const TKdTree *	GetKdTree () const

virtual MeasurementVectorSizeType	GetMeasurementVectorSize () const

virtual const char *	GetNameOfClass () const

const MembershipFunctionVectorObjectType *	GetOutput () const

virtual bool	GetUseClusterLabels () const

virtual void	SetCentroidPositionChangesThreshold (double _arg)

void	SetKdTree (TKdTree *tree)

virtual void	SetUseClusterLabels (bool _arg)

void	StartOptimization ()


virtual void	SetParameters (ParametersType _arg)

virtual ParametersType	GetParameters () const


virtual void	SetMaximumIteration (int _arg)

virtual int	GetMaximumIteration () const

Public Member Functions inherited from itk::Object
unsigned long	AddObserver (const EventObject &event, Command *)

unsigned long	AddObserver (const EventObject &event, Command *) const

virtual void	DebugOff () const

virtual void	DebugOn () const

Command *	GetCommand (unsigned long tag)

bool	GetDebug () const

MetaDataDictionary &	GetMetaDataDictionary (void)

const MetaDataDictionary &	GetMetaDataDictionary (void) const

virtual ModifiedTimeType	GetMTime () const

virtual const TimeStamp &	GetTimeStamp () const

bool	HasObserver (const EventObject &event) const

void	InvokeEvent (const EventObject &)

void	InvokeEvent (const EventObject &) const

virtual void	Modified () const

virtual void	Register () const

void	RemoveAllObservers ()

void	RemoveObserver (unsigned long tag)

void	SetDebug (bool debugFlag) const

void	SetMetaDataDictionary (const MetaDataDictionary &rhs)

virtual void	SetReferenceCount (int)

virtual void	UnRegister () const

Public Member Functions inherited from itk::LightObject
virtual void	Delete ()

virtual int	GetReferenceCount () const

	itkCloneMacro (Self)

void	Print (std::ostream &os, Indent indent=0) const

Static Public Member Functions
static Pointer	New ()

Static Public Member Functions inherited from itk::Object
static bool	GetGlobalWarningDisplay ()

static void	GlobalWarningDisplayOff ()

static void	GlobalWarningDisplayOn ()

static Pointer	New ()

static void	SetGlobalWarningDisplay (bool flag)

Static Public Member Functions inherited from itk::LightObject
static void	BreakOnError ()

static Pointer	New ()

Protected Member Functions
void	CopyParameters (InternalParametersType &source, InternalParametersType &target)

void	CopyParameters (ParametersType &source, InternalParametersType &target)

void	CopyParameters (InternalParametersType &source, ParametersType &target)

void	FillClusterLabels (KdTreeNodeType *node, int closestIndex)

void	Filter (KdTreeNodeType *node, std::vector< int > validIndexes, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)

int	GetClosestCandidate (ParameterType &measurements, std::vector< int > &validIndexes)

void	GetPoint (ParameterType &point, MeasurementVectorType measurements)

double	GetSumOfSquaredPositionChanges (InternalParametersType &previous, InternalParametersType &current)

bool	IsFarther (ParameterType &pointA, ParameterType &pointB, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)

	KdTreeBasedKmeansEstimator ()

void	PrintPoint (ParameterType &point)

void	PrintSelf (std::ostream &os, Indent indent) const

virtual	~KdTreeBasedKmeansEstimator ()

Protected Member Functions inherited from itk::Object
	Object ()

bool	PrintObservers (std::ostream &os, Indent indent) const

virtual void	SetTimeStamp (const TimeStamp &time)

virtual	~Object ()

Protected Member Functions inherited from itk::LightObject
virtual LightObject::Pointer	InternalClone () const

	LightObject ()

virtual void	PrintHeader (std::ostream &os, Indent indent) const

virtual void	PrintTrailer (std::ostream &os, Indent indent) const

virtual	~LightObject ()

Private Attributes
CandidateVector	m_CandidateVector

double	m_CentroidPositionChanges

double	m_CentroidPositionChangesThreshold

ClusterLabelsType	m_ClusterLabels

int	m_CurrentIteration

EuclideanDistanceMetric < ParameterType >::Pointer	m_DistanceMetric

bool	m_GenerateClusterLabels

TKdTree::Pointer	m_KdTree

int	m_MaximumIteration

MeasurementVectorSizeType	m_MeasurementVectorSize

MembershipFunctionVectorObjectPointer	m_MembershipFunctionsObject

ParametersType	m_Parameters

ParameterType	m_TempVertex

bool	m_UseClusterLabels

Additional Inherited Members
Protected Types inherited from itk::LightObject
typedef int	InternalReferenceCountType

Protected Attributes inherited from itk::LightObject
InternalReferenceCountType	m_ReferenceCount

SimpleFastMutexLock	m_ReferenceCountLock

Member Typedef Documentation

template<class TKdTree >

typedef KdTreeNodeType::CentroidType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CentroidType

Definition at line 99 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef itksys::hash_map< InstanceIdentifier, unsigned int > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ClusterLabelsType

Definition at line 162 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SmartPointer< const Self > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ConstPointer

Definition at line 85 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef DistanceToCentroidMembershipFunctionType::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::DistanceToCentroidMembershipFunctionPointer

Definition at line 116 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef DistanceToCentroidMembershipFunction< MeasurementVectorType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::DistanceToCentroidMembershipFunctionType

Typedef requried to generate dataobject decorated output that can be plugged into SampleClassifierFilter

Definition at line 113 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::InstanceIdentifier itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InstanceIdentifier

Definition at line 97 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef std::vector< ParameterType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::InternalParametersType

Definition at line 107 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::KdTreeNodeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeNodeType

Types for the KdTree data structure

Definition at line 91 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::MeasurementType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementType

Definition at line 95 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef unsigned int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorSizeType

Typedef for the length of a measurement vector

Definition at line 102 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::MeasurementVectorType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MeasurementVectorType

Definition at line 96 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef MembershipFunctionType::ConstPointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionPointer

Definition at line 119 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef MembershipFunctionBase< MeasurementVectorType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionType

Definition at line 118 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef MembershipFunctionVectorObjectType::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionVectorObjectPointer

Definition at line 124 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SimpleDataObjectDecorator< MembershipFunctionVectorType > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionVectorObjectType

Definition at line 122 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef std::vector< MembershipFunctionPointer > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::MembershipFunctionVectorType

Definition at line 120 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParametersType

Definition at line 108 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Array< double > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::ParameterType

Parameters type. It defines a position in the optimization search space.

Definition at line 106 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef SmartPointer< Self > itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Pointer

Definition at line 84 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef TKdTree::SampleType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SampleType

Definition at line 98 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef KdTreeBasedKmeansEstimator itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Self

Standard Self typedef.

Definition at line 82 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >

typedef Object itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Superclass

Definition at line 83 of file itkKdTreeBasedKmeansEstimator.h.

Constructor & Destructor Documentation

template<class TKdTree >

itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeBasedKmeansEstimator ( )

protected

template<class TKdTree >

virtual itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::~KdTreeBasedKmeansEstimator ( )

inlineprotectedvirtual

Definition at line 169 of file itkKdTreeBasedKmeansEstimator.h.

Member Function Documentation

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	InternalParametersType &	source,
		InternalParametersType &	target
	)

protected

copies the source parameters (k-means) to the target

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	ParametersType &	source,
		InternalParametersType &	target
	)

protected

copies the source parameters (k-means) to the target

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CopyParameters	(	InternalParametersType &	source,
		ParametersType &	target
	)

protected

copies the source parameters (k-means) to the target

template<class TKdTree >

virtual::itk::LightObject::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CreateAnother ( void ) const

virtual

Create an object from an instance, potentially deferring to a factory. This method allows you to create an instance of an object that is exactly the same type as the referring object. This is useful in cases where an object has been cast back to a base class.

Reimplemented from itk::Object.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::FillClusterLabels	(	KdTreeNodeType *	node,
		int	closestIndex
	)

protected

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::Filter	(	KdTreeNodeType *	node,
		std::vector< int >	validIndexes,
		MeasurementVectorType &	lowerBound,
		MeasurementVectorType &	upperBound
	)

protected

recursive pruning algorithm. the validIndexes vector contains only the indexes of the surviving candidates for the node

template<class TKdTree >

virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChanges ( ) const

virtual

template<class TKdTree >

virtual double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChangesThreshold ( ) const

virtual

template<class TKdTree >

int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClosestCandidate	(	ParameterType &	measurements,
		std::vector< int > &	validIndexes
	)

protected

get the index of the closest candidate to the measurements measurement vector

template<class TKdTree >

virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCurrentIteration ( ) const

virtual

template<class TKdTree >

const TKdTree* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetKdTree ( ) const

template<class TKdTree >

virtual int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMaximumIteration ( ) const

virtual

Set/Get maximum iteration limit.

template<class TKdTree >

virtual MeasurementVectorSizeType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMeasurementVectorSize ( ) const

virtual

Get the length of measurement vectors in the KdTree

template<class TKdTree >

virtual const char* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetNameOfClass ( ) const

virtual

Run-time type information (and related methods).

Reimplemented from itk::Object.

template<class TKdTree >

const MembershipFunctionVectorObjectType* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetOutput ( ) const

Output Membership function vector containing the membership functions with the final optimized parameters

template<class TKdTree >

virtual ParametersType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetParameters ( ) const

virtual

Set the position to initialize the optimization.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetPoint	(	ParameterType &	point,
		MeasurementVectorType	measurements
	)

protected

imports the measurements measurement vector data to the point

template<class TKdTree >

double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetSumOfSquaredPositionChanges	(	InternalParametersType &	previous,
		InternalParametersType &	current
	)

protected

gets the sum of squared difference between the previous position and current position of all centroid. This is the primary termination condition for this algorithm. If the return value is less than the value that was set by the SetCentroidPositionChangesThreshold method.

template<class TKdTree >

virtual bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetUseClusterLabels ( ) const

virtual

template<class TKdTree >

bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::IsFarther	(	ParameterType &	pointA,
		ParameterType &	pointB,
		MeasurementVectorType &	lowerBound,
		MeasurementVectorType &	upperBound
	)

protected

returns true if the pointA is farther than pointB to the boundary

template<class TKdTree >

static Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::New ( )

static

Method for creation through the object factory.

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintPoint ( ParameterType & point )

protected

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintSelf	(	std::ostream &	os,
		Indent	indent
	)		const

protectedvirtual

Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.

Reimplemented from itk::Object.

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetCentroidPositionChangesThreshold ( double _arg )

virtual

Set/Get the termination threshold for the squared sum of changes in centroid postions after one iteration

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetKdTree ( TKdTree * tree )

Set/Get the pointer to the KdTree

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetMaximumIteration ( int _arg )

virtual

Set/Get maximum iteration limit.

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetParameters ( ParametersType _arg )

virtual

Set the position to initialize the optimization.

template<class TKdTree >

virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetUseClusterLabels ( bool _arg )

virtual

template<class TKdTree >

void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::StartOptimization ( )

Start optimization Optimization will stop when it meets either of two termination conditions, the maximum iteration limit or epsilon (minimal changes in squared sum of changes in centroid positions)