#include <itkClassifierBase.h>
Inheritance diagram for itk::ClassifierBase< TDataContainer >:
ClassifierBase is the base class for the classifier objects. It provides the basic function definitions that are inherent to a classifier objects.
This is the SuperClass for the classifier framework. This is an abstract class defining an interface for all the classification objects available through the classifier framework in the ITK toolkit.
The basic functionality of the classifier framework base class is to classify each data point in given a data set to one of the N classes where N is either specified by the user or automatically determined using methods such as k-means clustering techniques.
The classifier framework supports two types of data where (1) The input is a series of data points that needs to be classified. These points may be in the form of images where the pixels can be viewed as a list and the spatial location of the pixels in not of a concern. This classification can be carried out via the use of SampleClassifier.
(2)The input is specifically an image and the pixels cannot be viewed as as a list of pixels since the spatial location of the pixel in needed for various algorithmic implementation such as Markov Random Field based approached. This type of data can be classified via the use of GeneralImageClassifierBase.
User will call The Update() function to run the classification proces
The user must generate the membership function before asking for data to be classified. One can automatically generate the membership function from the training data via the use of itkImageModelEstimator class. The membershio functions have to be populated by using the AddMembershipFunction If membership functions are not set prior to calling for classification, an exception in thrown.
As the method name indicates, you can have more than one membership function. One for each classes. The order you put the membership calculator becomes the class label for the class that is represented by the membership calculator.
The fourth argument is the type of decision rule. The main role of a decision rule is comparing the return values of the membership calculators. However, decision rule can include some prior knowledge that can improve the result. To plug in the decision rule, use SetDecisionRule method.
Before you call the Update method to start the classification process, you should plug in all necessary parts ( one or more membership functions, a decision rule, the unclassified data).
This class is templated over the container type.
Definition at line 86 of file itkClassifierBase.h.
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Type alias for decision rule Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >. Definition at line 114 of file itkClassifierBase.h. |
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Sets the decision rule Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >. Definition at line 103 of file itkClassifierBase.h. |
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Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >. Definition at line 108 of file itkClassifierBase.h. |
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Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >. Definition at line 111 of file itkClassifierBase.h. |
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Typedefs for membership funciton Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >. Definition at line 107 of file itkClassifierBase.h. |
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Standard class typedefs. Reimplemented from itk::LightProcessObject.
Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >. Definition at line 90 of file itkClassifierBase.h. |
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Reimplemented from itk::LightProcessObject.
Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >. Definition at line 91 of file itkClassifierBase.h. |
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Stores a membership function of a class in its internal vector |
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The real classification logic implementaion. All the subclasses of this class should implement this method. Reimplemented from itk::LightProcessObject.
Implemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >.
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Run-time type information (and related methods). Reimplemented from itk::LightProcessObject.
Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >.
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Gets the pointer to the decision rule being used. Definition at line 125 of file itkClassifierBase.h. |
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Gets the MembershipFunction that are plugged in by the AddMembershipFunction method. The index is assigned according to the order each membership function has been added using the AddMemberShipFunction method Definition at line 134 of file itkClassifierBase.h. |
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Gets the number of classes. |
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Gets the number of membership functions Definition at line 140 of file itkClassifierBase.h. |
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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.
Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >.
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Sets the pointer to the decision rule. Stores the decision rule that makes the real decision using informations from MembershipFunctions and other prior knowledge Definition at line 119 of file itkClassifierBase.h. |
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Sets the number of classes. |
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Starts the classification process |