I have two images, let's say old and new. In the old one there are 1:M objects, in the new one 1:N. I need to label the objects in the new one based on some metric (yes, I realize object tracking is a whole complicated area, but I need some   entry point right now). So I've obtained a metric based on the distance to the objects in the previous image, sort of 1-NN. I've also obtained a different metric, also sort of 1-NN, based on pixel intensity (color). Of course, I want to use both of these features somehow. For example, based on the distance metric object 3 in the new is closest to object 5 in the old, and based on the pixel intensity metric - closer to object 9 in the old image. Any suggestions on how to combine these two? Perhaps some kernel, but I'm not sure. 
Classification with two 1-NN classifiers
classification;image classification;sampling;object recognition