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
specified in create_class_knn by NumDim.
A handle to a k-NN data structure has to be specified in KNNHandle.
The feature vectors are collected in Features. The length of the
input vector must be a multiple of NumDim.
Each feature vector needs a class which can be given by ClassID,
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
train_class_knn.
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)
Handle of the k-NN classifier.
Features (input_control) number(-array) → (real)
List of features to add.
ClassID (input_control) integer(-array) → (integer)
Class IDs of the features.
If the parameters are valid, the operator add_sample_class_knn
returns the value TRUE. If necessary, an exception is raised.
train_class_knn,
read_class_knn
create_class_knn,
read_class_knn
Marius Muja, David G. Lowe: “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration”; International Conference on Computer Vision Theory and Applications (VISAPP 09); 2009.
Foundation