| Table of Contents / Classification / Gaussian Mixture Models | Operators |
add_sample_class_gmm — Add a training sample to the training data of a Gaussian Mixture Model.
add_sample_class_gmm adds a training sample to the Gaussian Mixture Model (GMM) given by GMMHandle. The training sample is given by Features and ClassID. Features is the feature vector of the sample, and consequently must be a real vector of length NumDim, as specified in create_class_gmm. ClassID is the class of the sample, an integer between 0 and NumClasses-1 (set in create_class_gmm).
In the special case where the feature vectors are of integer type, they are lying in the feature space in a grid with step width 1.0. For example, the RGB feature vectors typically used for color classification are triples having integer values between 0 and 255 for each of their components. In fact, there might be even several feature vectors representing the same point. When training a GMM with such data, the training algorithm may tend to align the modelled Gaussians along linearly dependent lines or planes of data that are parallel to the grid dimensions. If the number of Centers returned by train_class_gmm is unusually high, this indicates such a behavior of the algorithm. The parameter Randomize can be used to handle such undesired effects. If Randomize > 0.0, random Gaussian noise with mean 0 and standard deviation Randomize is added to each component of the training data vectors, and the transformed training data is stored in the GMM. For values of Randomize <= 1.0, the randomized data will look like small clouds around the grid points, which does not improve the properties of the data cloud. For values of Randomize >> 2.0, the randomization might have a too strong influence on the resulting GMM. For integer feature vectors, a value of Randomize between 1.5 and 2.0 is recommended, which transfoms the integer data into homogeneous clouds, without modifying its general form in the feature space. If the data has been created from integer data by scaling, the same problem may occur. Here, Randomize must be scaled with the same scale factor that was used to scale the original data.
Before the GMM can be trained with train_class_gmm, all training samples must be added to the GMM with add_sample_class_gmm.
The number of currently stored training samples can be queried with get_sample_num_class_gmm. Stored training samples can be read out again with get_sample_class_gmm.
Normally, it is useful to save the training samples in a file with write_samples_class_gmm to facilitate reusing the samples, and to facilitate that, if necessary, new training samples can be added to the data set, and hence to facilitate that a newly created GMM can be trained anew with the extended data set.
GMM handle.
Feature vector of the training sample to be stored.
Class of the training sample to be stored.
Standard deviation of the Gaussian noise added to the training data.
Default value: 0.0
Suggested values: 0.0, 1.5, 2.0
Restriction: Randomize >= 0.0
If the parameters are valid, the operator add_sample_class_gmm returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.
train_class_gmm, write_samples_class_gmm
read_samples_class_gmm, add_samples_image_class_gmm
clear_samples_class_gmm, get_sample_num_class_gmm, get_sample_class_gmm
Foundation
| Table of Contents / Classification / Gaussian Mixture Models | Operators |
| HALCON Reference Manual 10.0.2 | Copyright © 1996-2011 MVTec Software GmbH |