Perhaps a simpler case will make things clearer. Lets say we choose a 1x2 sample of pixels instead of 100x100.
Sample Pixels From the Image
+----+----+
| x1 | x2 |
+----+----+
Imagine when plotting our training set, we noticed that it can't be separated easily with a linear model, so we choose to add polynomial terms to better fit the data.
Let's say, we decide to construct our polynomials by including all of the pixel intensities, and all possible multiples that can be formed from them.
Since our matrix is small, let's enumerate them:
$$x_1,\ x_2,\ x_1^2,\ x_2^2,\ x_1 \times x_2,\ x_2 \times x_1 $$
Interpreting the above sequence of features can see that there is a pattern. The first two terms, group 1, are features consisting only of their pixel intensity. The following two terms after that, group 2, are features consisting of the square of their intensity. The last two terms, group 3, are the product of all the combinations of pairwise (two) pixel intensities.
group 1: $x_1,\ x_2$
group 2: $x_1^2,\ x_2^2$
group 3: $x_1 \times x_2,\ x_2 \times x_1$
But wait, there is a problem. If you look at the group 3 terms in the sequence ($ x_1 \times x_2$ and $x_2 \times x_1$) you'll notice that they are equal. Remember our housing example. Imagine having two features x1 = square footage, and x2 = square footage, for the same house... That doesn't make any sense! Ok, so we need to get rid of the duplicate feature, lets say arbitrarily $x_2 \times x_1$. Now we can rewrite the list of group three features as:
group 3: $x_1 \times x_2$
We count the features in all three groups and get 5.
But this is a toy example. Lets derive a generic formula for calculating the number of features. Let's use our original groups of features as a starting point.
$size group 1 + size group 2 + size group 3 = m \times n + m \times n +m \times n = 3 \times m \times n$
Ah! But we had to get rid of the duplicate product in group 3.
So to properly count the features for group 3 we will need a way to count all unique pairwise products in the matrix. Which can be done with the binomial coefficient, which is a method for counting all possible unique subgroups of size k from an equal or larger group of size n. So to properly count the features in group 3 calculate $C(m \times n, 2)$.
So our generic formula would be:
$$ m \times n + m \times n +C(m \times n, 2) = 2m \times n + C(m \times n, 2) $$
Lets use it to calculate the number of features in our toy example:
$$2 \times 1 \times 2 + C(1 \times 2, 2) = 4 + 1 = 5$$
Thats it!
Best Answer
You can compare images with different number of features (arising from images of different sizes). Pyramid Match Kernel does just that. It tries to measure the similarity between images that have different number of features. The code is available on the internet.