Name
add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm — Add training samples from an image to the training data of a support
vector machine.
add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm adds training samples from the
image ImageImageImageImageImageimage to the support vector machine (SVM) given by
SVMHandleSVMHandleSVMHandleSVMHandleSVMHandleSVMHandle. add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm is used to
store the training samples before training a classifier for the
pixel classification of multichannel images with
classify_image_class_svmclassify_image_class_svmClassifyImageClassSvmclassify_image_class_svmClassifyImageClassSvmClassifyImageClassSvm.
add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm works analogously to
add_sample_class_svmadd_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm.
The image ImageImageImageImageImageimage must have a number of channels equal to
NumFeatures, as specified with create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm. The
training regions for the NumClasses pixel classes are passed in
ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions. Hence, ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions must be a tuple
containing NumClasses regions. The order of the regions in
ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions determines the class of the pixels. If there
are no samples for a particular class in ImageImageImageImageImageimage, an empty
region must be passed at the position of the class in
ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions. With this mechanism it is possible to use
multiple images to add training samples for all relevant classes to
the SVM by calling add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm multiple
times with the different images and suitably chosen regions.
The regions in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions should contain representative
training samples for the respective classes. Hence, they need not
cover the entire image. The regions in ClassRegionsClassRegionsClassRegionsClassRegionsClassRegionsclassRegions should
not overlap each other, because this would lead to the fact that in
the training data the samples from the overlapping areas would be
assigned to multiple classes, which may lead to slower convergence
of the training and a lower classification performance.
A further application of this operator is the automatic novelty detection,
where, e.g., anomalies in color or texture can be detected. For this
mode a training set that defines a sample region (e.g., skin
regions for skin detection or samples of the correct texture) is
passed to the SVMHandleSVMHandleSVMHandleSVMHandleSVMHandleSVMHandle, which is created in the Mode
'novelty-detection'"novelty-detection""novelty-detection""novelty-detection""novelty-detection""novelty-detection". After training, regions that differ from the
trained sample regions are detected (e.g., the rejection class for skin
or errors in texture).
- Multithreading type: exclusive (runs in parallel only with independent operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
Regions of the classes to be trained.
If the parameters are valid add_samples_image_class_svmadd_samples_image_class_svmAddSamplesImageClassSvmadd_samples_image_class_svmAddSamplesImageClassSvmAddSamplesImageClassSvm
returns the value 2 (H_MSG_TRUE). If necessary, an exception is
raised.
create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm
train_class_svmtrain_class_svmTrainClassSvmtrain_class_svmTrainClassSvmTrainClassSvm,
write_samples_class_svmwrite_samples_class_svmWriteSamplesClassSvmwrite_samples_class_svmWriteSamplesClassSvmWriteSamplesClassSvm
read_samples_class_svmread_samples_class_svmReadSamplesClassSvmread_samples_class_svmReadSamplesClassSvmReadSamplesClassSvm
classify_image_class_svmclassify_image_class_svmClassifyImageClassSvmclassify_image_class_svmClassifyImageClassSvmClassifyImageClassSvm,
add_sample_class_svmadd_sample_class_svmAddSampleClassSvmadd_sample_class_svmAddSampleClassSvmAddSampleClassSvm,
clear_samples_class_svmclear_samples_class_svmClearSamplesClassSvmclear_samples_class_svmClearSamplesClassSvmClearSamplesClassSvm,
get_sample_num_class_svmget_sample_num_class_svmGetSampleNumClassSvmget_sample_num_class_svmGetSampleNumClassSvmGetSampleNumClassSvm,
get_sample_class_svmget_sample_class_svmGetSampleClassSvmget_sample_class_svmGetSampleClassSvmGetSampleClassSvm,
add_samples_image_class_mlpadd_samples_image_class_mlpAddSamplesImageClassMlpadd_samples_image_class_mlpAddSamplesImageClassMlpAddSamplesImageClassMlp
Foundation