Input: F(c) ∈ ℝN for all objects c, n, M, V, θ

Output: X̃(c) ∈ ℝ̃n

begin

0. Construct feature space F(c) ∈ ℝN, ∀c ∈ C (via feature extraction);

1. for m = 1 to M do

2. Calculate Xm (c) = CreateWeakEmbed(F(c)|ℱm, M, V), ∀c ∈ C, hence yielding ${ℝ}_m^n$;

3.   k=0;

4.   Calculate ${\psi }^{\mathit{\text{Acc}}}({ℝ}_m^n)$ (based on classification accuracy);

5.   if ${\psi }^{\mathit{\text{Acc}}}({ℝ}_m^n)>\theta$

6.     k++;

7.     Wk(i, j) = ‖Xm(c) − Xm(d)‖2 ∀c, d with indices i, j;

8.   endif

9. endfor

10. W̃(i, j) = MEDIANk [Wk(i, j)] ∀c, d;

11. Apply MDS to W̃ to obtain ℝ̃n;

12. Train a classifier on X̃(c) ∈ ℝ̃n, ∀c ∈ C, to distinguish object-class categories;

end