#include <itkImageKmeansModelEstimator.h>

Inheritance diagram for itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >:

Collaboration diagram for itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >:

Detailed Description

template<typename TInputImage, typename TMembershipFunction>
class itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >

Base class for ImageKmeansModelEstimator object.

itkImageKmeansModelEstimator generates the kmeans model (cluster centers). This object performs clustering of data sets into different clusters, either using user-provided seed points as an initial guess or generating the clusters using a recursive approach when the user provides the number of desired clusters. Each cluster is represented by its cluster center. The two algorithms used are the generalized Lloyd algorithm (GLA) and the Linde-Buzo-Gray algorithm. The cluster centers are also referred to as codewords and a table of cluster centers is referred as a codebook.

As required by the GLA algorithm, the initial seed cluster should contain approximate centers of clusters. The GLA algorithm genrates updated cluster centers that result in a lower distortion than the input seed cluster when the input vectors are mapped/classified/labelled using the given codebooks.

If no codebook is provided, the Linde-Buzo-Gray algorithm is used. This algorithm uses the GLA algorithm at its core to generate the centroids of the input vectors (data). However, since there is no initial codebook, LBG first creates a one word codebook (or centroid of one cluster comprising of all the input training vectors). The LBG uses codeword or centroid splitting to create an increasing number of clusters. Each new set of clusters are optimized using the GLA algorithm. The number of clusters increases as $2^{n}$ n= 0, 1, ... The codebook is expected to be in the form of a vnl matrix, where there are N rows, each row representing the cluster mean of a given cluster. The number of columns in the codebook should be equal to the input image vector dimension.

The threshold parameter controls the ''optimality'' of the returned codebook, where optimality is related to the least possible mean-squared error distortion that can be found by the algorithm. For larger thresholds, the result will be less optimal. For smaller thresholds, the result will be more optimal. If a more optimal result is desired, then the algorithm will take longer to complete. A reasonable threshold value is 0.01.

If, during the operation of the algorithm, there are any unused clusters or cells, the m_OffsetAdd and m_OffsetMultiply parameters are used to split the cells with the highest distortion. This function will attempt to fill empty cells up to 10 times (unless the overall distortion is zero). Using 0.01 is a reasonable default values for the m_OffsetAdd and m_OffsetMultiply parameters.

If the GLA is unable to resolve the data into the desired number of clusters or cells, only the codewords which were used will be returned.

In terms of clustering, codewords are cluster centers, and a codebook is a table containing all cluster centers. The GLA produces results that are equivalent to the K-means clustering algorithm.

For more information about the algorithms, see A. Gersho and R. M. Gray, {Vector Quantization and Signal Compression}, Kluwer Academic Publishers, Boston, MA, 1992.

This object supports data handling of multiband images. The object accepts the input image in vector format only, where each pixel is a vector and each element of the vector corresponds to an entry from 1 particular band of a multiband dataset. A single band image is treated as a vector image with a single element for every vector.

This function is templated over the type of input image. In addition, a second parameter for the MembershipFunction needs to be specified. In this case a Membership function that store cluster centroids models needs to be specified.

The Update() function enables the calculation of the various models, creates the membership function objects and populates them.

Note: There is a second implementation of k-means algorithm in ITK under the itk::Statistics namespace. While this algorithm (GLA/LBG based algorithm) is memory efficient, the other algorithm is time efficient.

See Also: KdTreeBasedKmeansEstimator, WeightedCentroidKdTreeGenerator, KdTree; ScalarImageKmeansImageFilter

Wiki Examples:

Definition at line 131 of file itkImageKmeansModelEstimator.h.

Public Types
using	CodebookMatrixOfDoubleType = vnl_matrix< double >

using	CodebookMatrixOfIntegerType = vnl_matrix< int >

using	ConstPointer = SmartPointer< const Self >

using	InputImageConstIterator = ImageRegionConstIterator< TInputImage >

using	InputImageConstPointer = typename TInputImage::ConstPointer

using	InputImageIterator = ImageRegionIterator< TInputImage >

using	InputImagePixelType = typename TInputImage::PixelType

using	InputImagePointer = typename TInputImage::Pointer

using	InputImageType = TInputImage

using	InputImageVectorType = typename TInputImage::PixelType::VectorType

using	MembershipFunctionPointer = typename TMembershipFunction::Pointer

using	Pointer = SmartPointer< Self >

using	Self = ImageKmeansModelEstimator

using	Superclass = ImageModelEstimatorBase< TInputImage, TMembershipFunction >

Public Types inherited from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >
using	ConstPointer = SmartPointer< const Self >

using	InputImagePointer = typename TInputImage::Pointer

using	InputImageType = TInputImage

using	MembershipFunctionPointer = typename TMembershipFunction::Pointer

using	MembershipFunctionPointerVector = std::vector< MembershipFunctionPointer >

using	Pointer = SmartPointer< Self >

using	Self = ImageModelEstimatorBase

using	Superclass = LightProcessObject

Public Types inherited from itk::LightProcessObject
using	ConstPointer = SmartPointer< const Self >

using	Pointer = SmartPointer< Self >

using	Self = LightProcessObject

using	Superclass = Object

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

using	Pointer = SmartPointer< Self >

using	Self = Object

using	Superclass = LightObject

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

using	Pointer = SmartPointer< Self >

using	Self = LightObject

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

virtual CodebookMatrixOfDoubleType	GetCodebook () const

CodebookMatrixOfDoubleType	GetKmeansResults ()

virtual int	GetMaxSplitAttempts () const

virtual const char *	GetNameOfClass () const

virtual double	GetOffsetAdd () const

virtual double	GetOffsetMultiply () const

CodebookMatrixOfDoubleType	GetOutCodebook ()

virtual double	GetThreshold () const

void	SetCodebook (CodebookMatrixOfDoubleType InCodebook)

virtual void	SetMaxSplitAttempts (int _arg)

virtual void	SetOffsetAdd (double _arg)

virtual void	SetOffsetMultiply (double _arg)

virtual void	SetThreshold (double _arg)

Public Member Functions inherited from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >
unsigned int	AddMembershipFunction (MembershipFunctionPointer function)

void	DeleteAllMembershipFunctions ()

const MembershipFunctionPointerVector	GetMembershipFunctions () const

unsigned int	GetNumberOfMembershipFunctions ()

virtual const unsigned int &	GetNumberOfModels () const

void	SetMembershipFunctions (MembershipFunctionPointerVector membershipFunctions)

virtual void	SetNumberOfModels (unsigned int _arg)

void	Update ()

virtual void	SetInputImage (InputImageType *_arg)

virtual InputImageType *	GetModifiableInputImage ()

virtual const InputImageType *	GetInputImage () const

Public Member Functions inherited from itk::LightProcessObject
virtual void	AbortGenerateDataOff ()

virtual void	AbortGenerateDataOn ()

virtual const bool &	GetAbortGenerateData () const

virtual void	SetAbortGenerateData (bool _arg)

virtual void	UpdateOutputData ()

void	UpdateProgress (float amount)

virtual void	SetProgress (float _arg)

virtual const float &	GetProgress () 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 ()

const MetaDataDictionary &	GetMetaDataDictionary () 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

void	Register () const override

void	RemoveAllObservers ()

void	RemoveObserver (unsigned long tag)

void	SetDebug (bool debugFlag) const

void	SetReferenceCount (int) override

void	UnRegister () const noexceptoverride

void	SetMetaDataDictionary (const MetaDataDictionary &rhs)

void	SetMetaDataDictionary (MetaDataDictionary &&rrhs)

virtual void	SetObjectName (std::string _arg)

virtual const std::string &	GetObjectName () 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::LightProcessObject
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	Allocate ()

void	GenerateData () override

	ImageKmeansModelEstimator ()

void	PrintKmeansAlgorithmResults ()

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

	~ImageKmeansModelEstimator () override=default

Protected Member Functions inherited from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >
void	GenerateData () override

	ImageModelEstimatorBase ()

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

	~ImageModelEstimatorBase () override=default

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

	~LightProcessObject () override

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

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

virtual void	SetTimeStamp (const TimeStamp &time)

	~Object () override

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 Types
using	ImageSizeType = typename TInputImage::SizeType

using	InputPixelVectorType = typename TInputImage::PixelType::VectorType

Private Member Functions
void	EstimateKmeansModelParameters ()

void	EstimateModels () override

void	NearestNeighborSearchBasic (double *distortion)

void	Perturb (double oldCodeword, int scale, double newCodeword)

void	Reallocate (int oldSize, int newSize)

void	SplitCodewords (int currentSize, int numDesired, int scale)

int	WithCodebookUseGLA ()

int	WithoutCodebookUseLBG ()

Private Attributes
CodebookMatrixOfDoubleType	m_Centroid

CodebookMatrixOfDoubleType	m_Codebook

CodebookMatrixOfDoubleType	m_CodewordDistortion

CodebookMatrixOfIntegerType	m_CodewordHistogram

SizeValueType	m_CurrentNumberOfCodewords

double	m_DoubleMaximum

int	m_MaxSplitAttempts

SizeValueType	m_NumberOfCodewords

double	m_OffsetAdd

double	m_OffsetMultiply

double	m_OutputDistortion

int	m_OutputNumberOfEmptyCells

double	m_Threshold

bool	m_ValidInCodebook

SizeValueType	m_VectorDimension

Additional Inherited Members
Protected Attributes inherited from itk::LightObject
std::atomic< int >	m_ReferenceCount