What characteristics does opencv's k nearest neighbor algorithm use to predict?

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Sorry, should have specified it was the first example.

If only vectors are passed to the function, how are they used for training and prediction with the k nearest neighbor algorithm?

In both cases, the training algorithm uses the passed data as features, it does not care what kind of features you feed in. But the difference is that, if you give the more discriminative features (resulted from a good feature extraction method), the training would be more effective and leads to more accuracy classification step afterward. I think it could be easier to understand to explain you with a binary SVM classification: the training step will generate an optimized superplan that distinguish the training samples (just two labels) on the features space, then the classification will use that superplan to predict the label for new test patterns. In the case of multilabels (N labels), the training step will generate N superplans for classification step.

Sorry, what are superplans? I've tried googling but nothing comes up.

I am sorry, because the length restriction so I can not finish the answer in one post. It is the SVM, and it is hyperplan,not superplan (it was a typo). And the SVM training algorithm has to solve an optimize problem to find the hyperplans but in the examples given, the KNearest is used to avoid that as follow: the training algorithm just "calculating weighted sum, and so on" (http://docs.opencv.org/opencv2refman.pdf) the data (training data with true labels) and the prediction will find k smallest distances to decide the result label of a test digit. In the first example, you have 10 digits (each one has 500 samples, 250 use for training and 250 for testing) and k=5. So one test sample is assigned to digit's label which has >k/2 smallest distances to test sample.

For more details on kNN: http://docs.opencv.org/trunk/doc/py_tutorials/py_ml/py_knn/py_knn_understanding/py_knn_understanding.html

http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

http://blog.damiles.com/2008/11/the-basic-patter-recognition-and-classification-with-opencv/

http://www.aishack.in/2010/10/k-nearest-neighbors-in-opencv/ http://bytefish.de/blog/machine_learning_opencv/

For more details on SVM:

http://en.wikipedia.org/wiki/Support_vector_machinehttp://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.htmlhttp://www.csie.ntu.edu.tw/~cjlin/libsvm/

Thank you very much for your help!