When they compare their method to SIFT, they use SIFT in a very funny way... They select 1000 keypoints at random from the training data, and then the feature vector for each novel image is the min distance from each selected training keypoint to some keypoint in the novel image (if I understand correctly). I've never heard of this being done before, and I wonder how it compares to the more standard ways of using SIFT for object recognition.

Also, I wonder how fast their implementation is.