ITK  4.1.0
Insight Segmentation and Registration Toolkit
Classes | Public Types | Public Member Functions | Static Public Member Functions | Protected Member Functions | Private Attributes
itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree > Class Template Reference

#include <itkKdTreeBasedKmeansEstimator.h>

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

List of all members.

Classes

class  CandidateVector
 Candidate Vector. More...

Public Types

typedef
KdTreeNodeType::CentroidType 
CentroidType
typedef itksys::hash_map
< InstanceIdentifier, unsigned
int > 
ClusterLabelsType
typedef SmartPointer< const SelfConstPointer
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< SelfPointer
typedef TKdTree::SampleType SampleType
typedef KdTreeBasedKmeansEstimator Self
typedef Object Superclass

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

Static Public Member Functions

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 ()

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

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:

Definition at line 77 of file itkKdTreeBasedKmeansEstimator.h.


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

Reimplemented from itk::Object.

Definition at line 85 of file itkKdTreeBasedKmeansEstimator.h.

Definition at line 116 of file itkKdTreeBasedKmeansEstimator.h.

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.

Definition at line 119 of file itkKdTreeBasedKmeansEstimator.h.

Definition at line 118 of file itkKdTreeBasedKmeansEstimator.h.

Definition at line 124 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object.

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.

Reimplemented from itk::Object.

Definition at line 82 of file itkKdTreeBasedKmeansEstimator.h.

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

Reimplemented from itk::Object.

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 ( ) [inline, protected, virtual]

Definition at line 168 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 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 >
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.

Reimplemented from itk::Object.

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 [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)

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)


Member Data Documentation

template<class TKdTree >
CandidateVector itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CandidateVector [private]

Definition at line 325 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CentroidPositionChanges [private]

sum of squared centroid position changes at the current iteration

Definition at line 310 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
double itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CentroidPositionChangesThreshold [private]

threshold for the sum of squared centroid position changes. termination criterion

Definition at line 314 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
ClusterLabelsType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_ClusterLabels [private]

Definition at line 331 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_CurrentIteration [private]

current number of iteration

Definition at line 304 of file itkKdTreeBasedKmeansEstimator.h.

pointer to the euclidean distance funtion

Definition at line 320 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_GenerateClusterLabels [private]

Definition at line 330 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
TKdTree::Pointer itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_KdTree [private]

pointer to the k-d tree

Definition at line 317 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
int itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_MaximumIteration [private]

maximum number of iteration. termination criterion

Definition at line 307 of file itkKdTreeBasedKmeansEstimator.h.

Definition at line 332 of file itkKdTreeBasedKmeansEstimator.h.

Definition at line 333 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
ParametersType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_Parameters [private]

k-means

Definition at line 323 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
ParameterType itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_TempVertex [private]

Definition at line 327 of file itkKdTreeBasedKmeansEstimator.h.

template<class TKdTree >
bool itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::m_UseClusterLabels [private]

Definition at line 329 of file itkKdTreeBasedKmeansEstimator.h.


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