#include <itkClassifierBase.h>
Inheritance diagram for itk::ClassifierBase:
itkClassifierBase 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 SetDecisionRulePointer method.
Before you call the GenerateData 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 function is templated over the measurement vector type. In the case of images this would be the measurement vector pixel type in the input image.
Definition at line 87 of file itkClassifierBase.h.
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Reimplemented from itk::LightProcessObject. Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >. Definition at line 94 of file itkClassifierBase.h. |
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Type alias for decision rule Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >. Definition at line 117 of file itkClassifierBase.h. |
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Set the decision rule Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::TableLookupSampleClassifier< TSample >. Definition at line 106 of file itkClassifierBase.h. |
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Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >. Definition at line 111 of file itkClassifierBase.h. |
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Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, and itk::Statistics::SampleClassifier< TSample >. Definition at line 114 of file itkClassifierBase.h. |
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Typedefs for membership funciton Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >. Definition at line 110 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::TableLookupSampleClassifier< TSample >. Definition at line 93 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::TableLookupSampleClassifier< TSample >. Definition at line 91 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::TableLookupSampleClassifier< TSample >. Definition at line 92 of file itkClassifierBase.h. |
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Stores a MembershipCalculator of a class in its internal vector |
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This method causes the filter to generate its output. Reimplemented from itk::LightProcessObject. Reimplemented in itk::ImageClassifierBase< TInputImage, TClassifiedImage >, itk::Statistics::SampleClassifier< TSample >, and itk::Statistics::TableLookupSampleClassifier< TSample >. |
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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::TableLookupSampleClassifier< TSample >. |
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Set the pointer to the classifer being used. Definition at line 128 of file itkClassifierBase.h. |
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Method to get mean Definition at line 134 of file itkClassifierBase.h. |
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Get the number of classes. |
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Method to get mean 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::TableLookupSampleClassifier< TSample >. |
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Set the pointer to the classifer being used. Stores the decision rule that makes the real decision using informations from MembershipFunctions and other prior knowledge Definition at line 122 of file itkClassifierBase.h. |
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Set the number of classes. |
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Define a virtual function to perform clustering of input data |