Why is image blurring called convolution and not cross correlation?
Broadly speaking, image blurring replaces each pixel value by a weighted sum of adjacent pixel values. Now, this weighted sum can be represented as convolution or as a correlation
for the simple reason that a convolution of $I$ and $g$  (where $I$ is the image and $g$ the
kernel) is the same as the correlation of $I$ and $\hat{g}$ where $\hat{g}$ is just
$g$ "flipped over". As fhucho has already pointed out, if the kernel is symmetric, the
flipping over has no effect.

It is easier to understand this idea in 1D rather than 2D.  For each $n$, suppose that
the result (weighted sum of adjacent values) that we need to get is
$$\begin{align}
\hat{I}[n] &amp;= \quad I[n]a[0]\\
&amp;\quad +\ I[n+1]a[1] + I[n+2]a[2] + \cdots + I[n+N]a[N]\\
&amp;\quad +\ I[n-1]a[-1] + I[n-2]a[-2] + \cdots + I[n-N]a[-N]\\
&amp;= \sum_{m=-N}^N I[n+m]a[m]
\end{align}$$
We readily recognize as the correlation of $\mathbf I$ and the sequence
$$\mathbf a =\left(a[-N], a[-N+1], \cdots, a[0], \cdots a[N-1], a[N]\right).$$
Now, define the sequence $\mathbf g$ as $\mathbf  a$ flipped over, that is,
$g[m] = a[-m]$ for all $m$. Note that $\mathbf a$ is just $\mathbf g$ flipped
over (and is thus the $\hat{g}$ mentioned earlier). Then we have that
$$\begin{align}
\hat{I}[n] &amp;= \sum_{m=-N}^N I[n+m]a[m]\\
&amp;= \sum_{m=-N}^N I[n+m]g[-m]\\
&amp;= \sum_{-i=-N}^N I[n-i]g[i] &amp;\text{replace}~ m~\text{by} ~ -i\\
&amp;= \sum_{i=-N}^N I[n-i]g[i] &amp; \text{write sum in reverse order}\\
&amp;= \mathbf I \star \mathbf g\bigr|_n
\end{align}$$
A similar calculation can be carried out for 2D signals (images) but
the details are messier and sometimes it is hard to see the forest
because of the trees.  

In summary, whether we choose to regard image blurring as
correlation or convolution
is entirely a matter of convenience and nomenclature.
We can convolve the image and the "impulse response"
or correlate the image with the "esnopser eslupmi",
whichever we prefer, and in either case, we are replacing
each pixel value with the same weighted sum of adjacent pixel values.