ITK
4.3.0
Insight Segmentation and Registration Toolkit
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#include <itkImageKmeansModelEstimator.h>
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.
Definition at line 131 of file itkImageKmeansModelEstimator.h.
Public Member Functions | |
virtual ::itk::LightObject::Pointer | CreateAnother (void) const |
virtual CodebookMatrixOfDoubleType | GetCodebook () const |
CodebookMatrixOfDoubleType | GetKmeansResults (void) |
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 () |
virtual InputImageType * | GetInputImage () |
const MembershipFunctionPointerVector | GetMembershipFunctions () const |
unsigned int | GetNumberOfMembershipFunctions () |
virtual const unsigned int & | GetNumberOfModels () |
virtual void | SetInputImage (InputImageType *_arg) |
void | SetMembershipFunctions (MembershipFunctionPointerVector membershipFunctions) |
virtual void | SetNumberOfModels (unsigned int _arg) |
void | Update () |
Public Member Functions inherited from itk::LightProcessObject | |
virtual void | AbortGenerateDataOff () |
virtual void | AbortGenerateDataOn () |
virtual const bool & | GetAbortGenerateData () |
virtual void | SetAbortGenerateData (bool _arg) |
virtual void | UpdateOutputData () |
void | UpdateProgress (float amount) |
virtual void | SetProgress (float _arg) |
virtual const float & | GetProgress () |
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 () |
Protected Member Functions | |
void | Allocate () |
void | GenerateData () |
ImageKmeansModelEstimator () | |
void | PrintKmeansAlgorithmResults () |
virtual void | PrintSelf (std::ostream &os, Indent indent) const |
~ImageKmeansModelEstimator () | |
Protected Member Functions inherited from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction > | |
ImageModelEstimatorBase () | |
void | PrintSelf (std::ostream &os, Indent indent) const |
~ImageModelEstimatorBase () | |
Protected Member Functions inherited from itk::LightProcessObject | |
LightProcessObject () | |
~LightProcessObject () | |
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 Types | |
typedef TInputImage::SizeType | ImageSizeType |
typedef TInputImage::PixelType::VectorType | InputPixelVectorType |
Private Member Functions | |
void | EstimateKmeansModelParameters () |
virtual void | EstimateModels () |
ImageKmeansModelEstimator (const Self &) | |
void | NearestNeighborSearchBasic (double *distortion) |
void | operator= (const Self &) |
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 () |
Additional Inherited Members | |
Protected Types inherited from itk::LightObject | |
typedef int | InternalReferenceCountType |
Protected Attributes inherited from itk::LightObject | |
InternalReferenceCountType | m_ReferenceCount |
SimpleFastMutexLock | m_ReferenceCountLock |
typedef vnl_matrix< double > itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::CodebookMatrixOfDoubleType |
Type definition for a double matrix.
Definition at line 170 of file itkImageKmeansModelEstimator.h.
typedef vnl_matrix< int > itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::CodebookMatrixOfIntegerType |
Type definition for an integer vector.
Definition at line 173 of file itkImageKmeansModelEstimator.h.
typedef SmartPointer< const Self > itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::ConstPointer |
Definition at line 141 of file itkImageKmeansModelEstimator.h.
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Definition at line 239 of file itkImageKmeansModelEstimator.h.
typedef ImageRegionConstIterator< TInputImage > itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::InputImageConstIterator |
Definition at line 164 of file itkImageKmeansModelEstimator.h.
typedef TInputImage::ConstPointer itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::InputImageConstPointer |
Definition at line 152 of file itkImageKmeansModelEstimator.h.
typedef ImageRegionIterator< TInputImage > itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::InputImageIterator |
Type definition for the input image iterator type.
Definition at line 162 of file itkImageKmeansModelEstimator.h.
typedef TInputImage::PixelType itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::InputImagePixelType |
Type definition for the input image pixel type.
Definition at line 159 of file itkImageKmeansModelEstimator.h.
typedef TInputImage::Pointer itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::InputImagePointer |
Definition at line 151 of file itkImageKmeansModelEstimator.h.
typedef TInputImage itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::InputImageType |
Type definition for the input image.
Definition at line 147 of file itkImageKmeansModelEstimator.h.
typedef TInputImage::PixelType::VectorType itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::InputImageVectorType |
Type definition for the vector associated with input image pixel type.
Definition at line 156 of file itkImageKmeansModelEstimator.h.
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Set up the vector to store the image data.
Definition at line 242 of file itkImageKmeansModelEstimator.h.
typedef TMembershipFunction::Pointer itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::MembershipFunctionPointer |
Type definitions for the membership function .
Definition at line 167 of file itkImageKmeansModelEstimator.h.
typedef SmartPointer< Self > itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::Pointer |
Definition at line 140 of file itkImageKmeansModelEstimator.h.
typedef ImageKmeansModelEstimator itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::Self |
Standard class typedefs.
Definition at line 136 of file itkImageKmeansModelEstimator.h.
typedef ImageModelEstimatorBase< TInputImage, TMembershipFunction > itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::Superclass |
Definition at line 138 of file itkImageKmeansModelEstimator.h.
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Allocate memory for the output model.
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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::LightProcessObject.
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A function that generates the cluster centers (model) corresponding to the estimates of the cluster centers (in the initial codebook). If no codebook is provided, then use the number of classes to determine the cluster centers or the Kmeans model. This is the the base function to call the K-means classifier.
Implements itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.
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Starts the image modelling process
Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.
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Get the cluster centers.
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Return the codebook/cluster centers.
Definition at line 209 of file itkImageKmeansModelEstimator.h.
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Get the manimum number of attempts to split a codeword.
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Run-time type information (and related methods).
Reimplemented from itk::ImageModelEstimatorBase< TInputImage, TMembershipFunction >.
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Get the offset add parameter.
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Get the offset multiplication parameter.
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Get the optimized codebook or the centroids of the clusters.
Definition at line 182 of file itkImageKmeansModelEstimator.h.
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Get the threshold parameter.
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Method for creation through the object factory.
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Print out the results on the screen for visual feedback.
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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::LightProcessObject.
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void itk::ImageKmeansModelEstimator< TInputImage, TMembershipFunction >::SetCodebook | ( | CodebookMatrixOfDoubleType | InCodebook | ) |
Set the cluster centers.
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Set the maximum number of attempts to split a codeword.
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Set the offset add parameter.
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Set the offset multiplication parameter.
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Set the threshold parameter.
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Definition at line 264 of file itkImageKmeansModelEstimator.h.
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Definition at line 261 of file itkImageKmeansModelEstimator.h.
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Definition at line 282 of file itkImageKmeansModelEstimator.h.
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Definition at line 281 of file itkImageKmeansModelEstimator.h.
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Definition at line 279 of file itkImageKmeansModelEstimator.h.
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Definition at line 273 of file itkImageKmeansModelEstimator.h.
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Definition at line 269 of file itkImageKmeansModelEstimator.h.
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Definition at line 278 of file itkImageKmeansModelEstimator.h.
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Definition at line 267 of file itkImageKmeansModelEstimator.h.
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Definition at line 268 of file itkImageKmeansModelEstimator.h.
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Definition at line 274 of file itkImageKmeansModelEstimator.h.
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Definition at line 275 of file itkImageKmeansModelEstimator.h.
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Definition at line 266 of file itkImageKmeansModelEstimator.h.
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Definition at line 272 of file itkImageKmeansModelEstimator.h.
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Definition at line 277 of file itkImageKmeansModelEstimator.h.