itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > Class Template Reference

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

#include <itkKdTreeBasedKmeansEstimator.h>

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

[legend]Collaboration diagram for itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >:

[legend]List of all members.

Public Types
typedef KdTreeBasedKmeansEstimator	Self
typedef Object	Superclass
typedef SmartPointer< Self >	Pointer
typedef SmartPointer< const Self >	ConstPointer
typedef TKdTree::KdTreeNodeType	KdTreeNodeType
typedef TKdTree::MeasurementType	MeasurementType
typedef TKdTree::MeasurementVectorType	MeasurementVectorType
typedef TKdTree::InstanceIdentifier	InstanceIdentifier
typedef TKdTree::SampleType	SampleType
typedef KdTreeNodeType::CentroidType	CentroidType
typedef unsigned int	MeasurementVectorSizeType
typedef Array< double >	ParameterType
typedef std::vector< ParameterType >	InternalParametersType
typedef Array< double >	ParametersType
typedef itk::hash_map< InstanceIdentifier, unsigned int >	ClusterLabelsType
Public Member Functions
virtual const char *	GetNameOfClass () const
void	SetParameters (ParametersType &params)
ParametersType &	GetParameters ()
TKdTree *	GetKdTree ()
virtual const MeasurementVectorSizeType &	GetMeasurementVectorSize ()
virtual const int &	GetCurrentIteration ()
virtual const double &	GetCentroidPositionChanges ()
void	StartOptimization ()
void	SetUseClusterLabels (bool flag)
ClusterLabelsType *	GetClusterLabels ()
virtual void	SetMaximumIteration (int _arg)
virtual const int &	GetMaximumIteration ()
virtual void	SetCentroidPositionChangesThreshold (double _arg)
virtual const double &	GetCentroidPositionChangesThreshold ()
void	SetKdTree (TKdTree *tree)
Static Public Member Functions
static Pointer	New ()
Protected Member Functions
	KdTreeBasedKmeansEstimator ()
virtual	~KdTreeBasedKmeansEstimator ()
void	PrintSelf (std::ostream &os, Indent indent) const
void	FillClusterLabels (KdTreeNodeType *node, int closestIndex)
double	GetSumOfSquaredPositionChanges (InternalParametersType &previous, InternalParametersType &current)
int	GetClosestCandidate (ParameterType &measurements, std::vector< int > &validIndexes)
bool	IsFarther (ParameterType &pointA, ParameterType &pointB, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
void	Filter (KdTreeNodeType *node, std::vector< int > validIndexes, MeasurementVectorType &lowerBound, MeasurementVectorType &upperBound)
void	CopyParameters (InternalParametersType &source, InternalParametersType &target)
void	CopyParameters (ParametersType &source, InternalParametersType &target)
void	CopyParameters (InternalParametersType &source, ParametersType &target)
void	PrintPoint (ParameterType &point)
void	GetPoint (ParameterType &point, MeasurementVectorType measurements)
Classes
class	CandidateVector

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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

Definition at line 67 of file itkKdTreeBasedKmeansEstimator.h.

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Member Typedef Documentation

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

Definition at line 89 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 145 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object. Definition at line 75 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 87 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 98 of file itkKdTreeBasedKmeansEstimator.h.

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

Types for the KdTree data structure Definition at line 81 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 85 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 93 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 86 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 99 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 97 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object. Definition at line 74 of file itkKdTreeBasedKmeansEstimator.h.

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

Definition at line 88 of file itkKdTreeBasedKmeansEstimator.h.

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

Standard "Self" typedef. Reimplemented from itk::Object. Definition at line 72 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object. Definition at line 73 of file itkKdTreeBasedKmeansEstimator.h.

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Constructor & Destructor Documentation

template<class TKdTree> itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::KdTreeBasedKmeansEstimator (   )  [protected]

template<class TKdTree> virtual itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::~KdTreeBasedKmeansEstimator (   )  [inline, protected, virtual]

Definition at line 155 of file itkKdTreeBasedKmeansEstimator.h.

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Member Function Documentation

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> 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, InternalParametersType &  target )  [protected]

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

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

template<class TKdTree> virtual const double& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCentroidPositionChangesThreshold (   )  [virtual]

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

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> ClusterLabelsType* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetClusterLabels (   )  [inline]

Definition at line 150 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> virtual const int& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetCurrentIteration (   )  [virtual]

template<class TKdTree> TKdTree* itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetKdTree (   )  [inline]

Definition at line 130 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> virtual const int& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMaximumIteration (   )  [virtual]

Set/Get maximum iteration limit.

template<class TKdTree> virtual const MeasurementVectorSizeType& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetMeasurementVectorSize (   )  [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> ParametersType& itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetParameters (  void   )  [inline]

Get current position of the optimization. Definition at line 106 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::GetPoint (  ParameterType &  point, MeasurementVectorType  measurements )  [inline, protected]

imports the "measurements" measurement vector data to the "point" Definition at line 279 of file itkKdTreeBasedKmeansEstimator.h.

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 postion 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> 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. Reimplemented from itk::Object.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintPoint (  ParameterType &  point  )  [inline, protected]

Definition at line 289 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::PrintSelf (  std::ostream &  os, Indent  indent )  const [protected, virtual]

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  )  [inline]

Set/Get the pointer to the KdTree Definition at line 121 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> virtual void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetMaximumIteration (  int  _arg  )  [virtual]

Set/Get maximum iteration limit.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetParameters (  ParametersType &  params  )  [inline]

Set the position to initialize the optimization. Definition at line 102 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree> void itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::SetUseClusterLabels (  bool  flag  )  [inline]

Definition at line 147 of file itkKdTreeBasedKmeansEstimator.h.

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)

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

• itkKdTreeBasedKmeansEstimator.h

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