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.
Handle of the k-NN classifier.
List of features to add.
Class IDs of the features.
If the parameters are valid, the operator add_sample_class_knn returns the value 2 (H_MSG_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.