Proposals:Refactoring Statistics Framework 2007 Background: Difference between revisions
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[[Image:DudaClassifier.png]] | [[Image:DudaClassifier.png]] | ||
[[Image:StatisticalClassificationFramework.png]] | [[Image:StatisticalClassificationFramework.png]] | ||
The main components of a classification framework are | |||
# Input | |||
## Image | |||
## Data points | |||
#Membership function | |||
## Distance functions | |||
## Can be manually set or automatically generated from the sample data | |||
## Estimators are available to generate membership functions ( ImageModelEstimatorBase, ImageGuassianModelEstimator,ExpectationMaximizationMixtureModelEstimator ) | |||
## Some classes are named with Estimator suffix but they do more than just estimating membership functions | |||
### itkKdTreeBasedKmeansEstimator | |||
# Decison Rule (Classifier ): such as | |||
Typical scenario | |||
# Use an estimator to generate class models for input data. | |||
# Use the generated class models, distance function and a decision rule to determine which class your | |||
input belongs to. | |||
Revision as of 19:55, 16 July 2008
The main components of a classification framework are
- Input
- Image
- Data points
- Membership function
- Distance functions
- Can be manually set or automatically generated from the sample data
- Estimators are available to generate membership functions ( ImageModelEstimatorBase, ImageGuassianModelEstimator,ExpectationMaximizationMixtureModelEstimator )
- Some classes are named with Estimator suffix but they do more than just estimating membership functions
- itkKdTreeBasedKmeansEstimator
- Decison Rule (Classifier ): such as
Typical scenario
- Use an estimator to generate class models for input data.
- Use the generated class models, distance function and a decision rule to determine which class your
input belongs to.
