(A "church feature"? Now, that is an interesting idea...)

To me, the results are convincing specially if you consider that the intended use
of this is for image retrieval, not for image recognition. So speed is very important,
and filtering false positives might not be much of a problem.

But some of the theoretical foundations of the paper appear to be loose:

- Details are likely to be lost for the next level of filtering. This means that
looking for global structures that are composed of distributed details is not
possible: tiger stripes can be detected, but only because stripes are contiguous.

- Although the filtering process is described as non-linear, they pass
three filters in order to get the final feature map.

So I am not sure if the the theoretical foundation they offer is good
enough for all kind of images.