At the moment I am using OpenCV's KNN implementation to classify images. It currently classifies images into P, S or rectangle, and correctly. However if I feed it an image of noise it will attempt to classify it as 1 of the 3 classifications I stated earlier. To get it to classify as noise, should I train the KNN to put noise in a 'noise' category, or is there some kind of accuracy rating I can use?
The way to do it is to use the dists variable in the knn_nearest function. It spits out the distance between your vector and the K unit vectors, the further the distance the less they have in common with the test data.