Computational Information Geometry Wonderland

Matching feature descriptors in vision is essential for stitching and object recognition among others. Since SIFT is based on discretizing the 360-degree wheel of gradient at different scales, it is better to use circular earth mover distance than a straight EMD.

Experiments are reported Circular Earth Mover?s Distance for the comparison of local features

I write twice this post. When I pushed on the submit button, all my message was erased. This is a big frustration to start a day with such an accident -:)!

Ok, the Earth Mover Distance (EMD) distance introduced in 1997 by Stanford CS group, is in fact known to statisticians under the name of Mallows distance:

It coincides exactly for normalized histograms but not for unormalized distributions. I recommend reading ICCCV'01's paper for a nice description of these similitudes:
The Earth Mover's distance is the Mallows distance: some insights from statistics

Ok, I push the "preview" button and cross fingers for not encountering the same problem twice -:)