Image Cross-Correlation interpretation
In the case of 2 images $m_{1}(x,y)$ and $m_{2}(x,y)$ of size $m×n$ , where $(x,y)$ are the coordinates of the image, the correlation constructs a matrix $A$ where each $A(i,j)$ is the degree of similarity between the image $m_{1}(x,y)$ and $m_{2}(x+i,y+j)$ (note that $m_{2}(x+i,y+i)$ is the image $m_{2}(x,y)$ translated $i$, $j$ pixels). This degree of similarity is basically a measure of the angle between the vectors $v_{1}$ and $v_{2}$ of $R^{m×n}$ that could be constructed by vectoring the images $m_{1}(x,y)$ and $m_{2}(x+i,y+j)$, this is because for each $i, j$ What the correlation really does is the normalized dot product $\frac{v_{1}.v_{2}}{|v_{1}||v_{2}|}$. Remember that $v_{1}.v_{2}=|v_{1}||v_{2}|cos(θ)$, where θ is the angle between the vectors $v_{1}$ and $v_{2}$.

In conclusion, the maximum $A(i,j)$ tells you that for the degree of similarity (angle θ) between $m_{1}(x,y)$ and $m_{2}(x,y)$ to be maximized, $m_{2}(x,y)$ must be translated $(i, j)$ pixels.

This measure of similarity is not very good, since it is not robust to changes in tones, lighting, noise, among others. There are other comparison alternatives such as PCA, SRC, SIFT or SURF.