  Let $\mathcal{D}$ be the dictionary of Gaussian mixtures: the functions
created by affine change of variables of a single Gaussian in $n$ dimensions.
$\mathcal{D}$ is used pervasively in scientific applications to a degree that
practitioners often employ it as their default choice for representing their
scientific object. Its use in applications hinges on the perception that this
dictionary is large enough, and its members are local enough in space and
frequency, to provide efficient approximation to "almost all objects of
interest". However, and perhaps surprisingly, only a handful of concrete
theoretical results are actually known on the ability to use Gaussian mixtures
in lieu of mainstream representation systems. The present paper shows that, in
2D, Gaussian mixtures are effective in resolving anisotropic structures, too.
In this setup, the "smoothness class" is comprised of 2D functions that are
sparsely represented using curvelets. An algorithm for $N$-term approximation
from (a small subset of) $\mathcal{D}$ is presented, and the error bounds are
then shown to be on par with the errors of $N$-term curvelet approximation. The
latter are optimal, essentially by definition. Our approach is based on
providing effective approximation from $\mathcal{D}$ to the members of the
curvelet system, mimicking the approach in arXiv:0802.2517 and arXiv:0911.2803
where the mother wavelets are approximated. When the error is measured in the
$1$-norm, this adaptation of the prior approach, combined with standard tools,
yields the desired results. However, handling the $2$-norm case is much more
subtle and requires substantial new machinery: in this case, the error analysis
cannot be solely done on the space domain: some of it has to be carried out on
frequency. Since, on frequency, all members of $\mathcal{D}$ are centered at
the origin, a delicate analysis for controlling the error there is needed.
