add_samples_image_class_mlp🔗
Short description🔗
add_samples_image_class_mlp — Add training samples from an image to the training data of a
multilayer perceptron.
Signature🔗
add_samples_image_class_mlp( image Image, region ClassRegions, class_mlp MLPHandle )
Description🔗
add_samples_image_class_mlp adds training samples from the
image Image to the multilayer perceptron (MLP) given by
MLPHandle. add_samples_image_class_mlp is used to
store the training samples before a classifier to be used for the
pixel classification of multichannel images with
classify_image_class_mlp is trained.
add_samples_image_class_mlp works analogously to
add_sample_class_mlp. Because here the MLP is always used
for classification, OutputFunction \(=\) 'softmax'
must be specified when the MLP is created with
create_class_mlp. The image Image must have a
number of channels equal to NumInput, as specified with
create_class_mlp. The training regions for the
NumOutput pixel classes are passed in
ClassRegions. Hence, ClassRegions must be a tuple
containing NumOutput regions. The order of the regions in
ClassRegions determines the class of the pixels. If there
are no samples for a particular class in Image an empty
region must be passed at the position of the class in
ClassRegions. With this mechanism it is possible to use
multiple images to add training samples for all relevant classes to
the MLP by calling add_samples_image_class_mlp multiple
times with the different images and suitably chosen regions. The
regions in ClassRegions should contain representative
training samples for the respective classes. Hence, they need not
cover the entire image. The regions in ClassRegions 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.
Execution information🔗
Execution information
-
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
-
Multithreading scope: global (may be called from any thread).
-
Processed without parallelization.
This operator modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
Parameters🔗
Image (input_object) (multichannel-)image → object (byte / cyclic / direction / int1 / int2 / uint2 / int4 / real)
Training image.
ClassRegions (input_object) region-array → object
Regions of the classes to be trained.
MLPHandle (input_control, state is modified) class_mlp → (handle)
MLP handle.
Result🔗
If the parameters are valid, the operator
add_samples_image_class_mlp returns the value 2 (H_MSG_TRUE). If
necessary an exception is raised.
Combinations with other operators🔗
Combinations
Possible predecessors
Possible successors
train_class_mlp, write_samples_class_mlp
Alternatives
See also
classify_image_class_mlp, add_sample_class_mlp, clear_samples_class_mlp, get_sample_num_class_mlp, get_sample_class_mlp, add_samples_image_class_svm
Module🔗
Foundation