Edited by author 11-09-2006 01:37 PM
It's somewhat disappointing that during vocabulary construction their clustering reduced the number of parts by only about a third. Given the success of a couple of the features (tires), this seems to imply that most features did not find a good match in the other 49 images in the training set. This makes me somewhat surprised that you'd see the huge decrease in performance when you remove the clustering stage that you see in figure 10. I wonder how things would change if they increased the number of interest points; it looks like they only get about 8 per image, which seems small particularly if you're going to cluster the points. Using a different measure of similarity might also improve clustering - correlation isn't shift invariant.