I have two questions:

1) I was wondering if other similarity measures have been used in this problem (as well as in the Pilu paper). The algorithms presented do a remarkable job using these simple similarity measures, but they are not perfect. Certainly, as argued in the paper, an attempt can be made to have a higher level combine the results of these algorithms into a single object (e.g., combining the zebra pieces into a zebra), however is this a better approach than having a better (but more complex) similarity measure at the lower level? So the first question is, how about using a very complex similarity measure, such as distance between Gabor jets (vectors composed of coefficients of dot products of the image at each location with a rosette of Gabor wavelets), or, to be even more complex, using a set of ICA derived filters instead of Gabors?

Also, this leads me to the second question:
2) Do recognition algorithms that first do segmentation and then do recognition perform better than algorithms that don't include segmentation as an explicit step? Where else has segmentation followed by some other algorithm proven to be more effective than the best a non-segmentation-using algorithm can do?