add_sample_class_knn — Add a sample to a k-nearest neighbors (k-NN) classifier.
add_sample_class_knn adds a feature vector to a k-nearest neighbors
(k-NN) data structure. The length of a feature vector was
A handle to a k-NN data structure has to be specified in
The feature vectors are collected in
Features. The length of the
input vector must be a multiple of
Each feature vector needs a class which can be given by
if only one was specified, the class is used for all vectors. The class is a
natural number greater or equal to 0. If only one class is used, the
class has to be 0. In case the operator
classify_image_class_knn will be used, all numbers starting from
0 to the number of classes-1 should be used, since otherwise an empty
region will be generated for each unused number.
It is allowed to add samples to an already trained k-NN classificator.
The new data is only integrated after another call to
If the k-NN classifier has been trained with automatic feature normalization
enabled, the supplied features
Features are interpreted as
unnormalized and are normalized as it was defined by the last call to
train_class_knn. Please see
train_class_knn for more
information on normalization.
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.
KNNHandle(input_control, state is modified) class_knn
Handle of the k-NN classifier.
List of features to add.
Class IDs of the features.
If the parameters are valid, the operator
returns the value TRUE. If necessary, an exception is raised.
Marius Muja, David G. Lowe: “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”; International Conference on Computer Vision Theory and Applications (VISAPP 09); 2009.