Operator Reference

HALCON Operator Reference / Classification / Gaussian Mixture Models

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classify_class_gmmT_classify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm (Operator)

classify_class_gmmT_classify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm — Calculate the class of a feature vector by a Gaussian Mixture Model.

Signature

classify_class_gmm( : : GMMHandle, Features, Num : ClassID, ClassProb, Density, KSigmaProb)

Herror T_classify_class_gmm(const Htuple GMMHandle, const Htuple Features, const Htuple Num, Htuple* ClassID, Htuple* ClassProb, Htuple* Density, Htuple* KSigmaProb)

void ClassifyClassGmm(const HTuple& GMMHandle, const HTuple& Features, const HTuple& Num, HTuple* ClassID, HTuple* ClassProb, HTuple* Density, HTuple* KSigmaProb)

HTuple HClassGmm::ClassifyClassGmm(const HTuple& Features, Hlong Num, HTuple* ClassProb, HTuple* Density, HTuple* KSigmaProb) const

static void HOperatorSet.ClassifyClassGmm(HTuple GMMHandle, HTuple features, HTuple num, out HTuple classID, out HTuple classProb, out HTuple density, out HTuple KSigmaProb)

HTuple HClassGmm.ClassifyClassGmm(HTuple features, int num, out HTuple classProb, out HTuple density, out HTuple KSigmaProb)

def classify_class_gmm(gmmhandle: HHandle, features: Sequence[float], num: int) -> Tuple[Sequence[int], Sequence[float], Sequence[float], Sequence[float]]

def classify_class_gmm_s(gmmhandle: HHandle, features: Sequence[float], num: int) -> Tuple[int, Sequence[float], Sequence[float], Sequence[float]]

Description

classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm computes the best NumNumNumnumnum classes of the feature vector FeaturesFeaturesFeaturesfeaturesfeatures with the Gaussian Mixture Model (GMM) GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle and returns the classes in ClassIDClassIDClassIDclassIDclass_id and the corresponding probabilities of the classes in ClassProbClassProbClassProbclassProbclass_prob. Before calling classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm, the GMM must be trained with train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmtrain_class_gmm.

classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm corresponds to a call to evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm and an additional step that extracts the best NumNumNumnumnum classes. As described with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm, the output values of the GMM can be interpreted as probabilities of the occurrence of the respective classes. However, here the posterior probability ClassProbClassProbClassProbclassProbclass_prob is further normalized as ClassProbClassProbClassProbclassProbclass_prob = p(i|x)/p(x) , where p(i|x) and p(x) are specified with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm. In most cases it should be sufficient to use NumNumNumnumnum = 1 in order to decide whether the probability of the best class is high enough. In some applications it may be interesting to also take the second best class into account (NumNumNumnumnum = 2), particularly if it can be expected that the classes show a significant degree of overlap.

DensityDensityDensitydensitydensity and KSigmaProbKSigmaProbKSigmaProbKSigmaProbksigma_prob are explained with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm.

Execution Information

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

Parameters

GMMHandleGMMHandleGMMHandleGMMHandlegmmhandle (input_control)  class_gmm → HClassGmm, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

GMM handle.

FeaturesFeaturesFeaturesfeaturesfeatures (input_control)  real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Feature vector.

NumNumNumnumnum (input_control)  integer → HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of best classes to determine.

Default: 1

Suggested values: 1, 2, 3, 4, 5

ClassIDClassIDClassIDclassIDclass_id (output_control)  integer(-array) → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Result of classifying the feature vector with the GMM.

ClassProbClassProbClassProbclassProbclass_prob (output_control)  real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

A-posteriori probability of the classes.

DensityDensityDensitydensitydensity (output_control)  real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Probability density of the feature vector.

KSigmaProbKSigmaProbKSigmaProbKSigmaProbksigma_prob (output_control)  real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)

Normalized k-sigma-probability for the feature vector.

Result

If the parameters are valid, the operator classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmclassify_class_gmm returns the value 2 ( H_MSG_TRUE) . If necessary an exception is raised.

Possible Predecessors

train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmtrain_class_gmm, read_class_gmmread_class_gmmReadClassGmmReadClassGmmread_class_gmm

Alternatives

evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmevaluate_class_gmm

See also

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmcreate_class_gmm

References

Christopher M. Bishop: “Neural Networks for Pattern Recognition”; Oxford University Press, Oxford; 1995.
Mario A.T. Figueiredo: “Unsupervised Learning of Finite Mixture Models”; IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 3; March 2002.

Module

Foundation

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