  Regular decompositions are necessary for most superpixel-based object
recognition or tracking applications. So far in the literature, the regularity
or compactness of a superpixel shape is mainly measured by its circularity. In
this work, we first demonstrate that such measure is not adapted for superpixel
evaluation, since it does not directly express regularity but circular
appearance. Then, we propose a new metric that considers several shape
regularity aspects: convexity, balanced repartition, and contour smoothness.
Finally, we demonstrate that our measure is robust to scale and noise and
enables to more relevantly compare superpixel methods.
