10. The process of claim 9 , wherein each pair-wise SDF f i (k) is given by the equation f ⁡ ( x , y ) = ∑ i = 1 n ⁢ W ii ⁡ ( K ⁡ ( x , x i ) - K ⁡ ( y , x i ) ) 2 , wherein, x is a feature vector for a training image under consideration, y is a feature vector for the new image, W is a diagonal matrix with non-negative entries, and K( ) is a kernel given by the equation K(x,y)=φ(x)φ(y), wherein φ( ) is a mapping function which maps a given feature vector to a very high dimensional vector.
10. The process of claim 9 , wherein each pair-wise SDF f i (k) is given by the equation f ⁡ ( x , y ) = ∑ i = 1 n ⁢ W ii ⁡ ( K ⁡ ( x , x i ) - K ⁡ ( y , x i ) ) 2 , wherein, x is a feature vector for a training image under consideration, y is a feature vector for the new image, W is a diagonal matrix with non-negative entries, and K( ) is a kernel given by the equation K(x,y)=φ(x)φ(y), wherein φ( ) is a mapping function which maps a given feature vector to a very high dimensional vector. 11. The process of claim 10 , wherein the diagonal matrix W is computed using the quadratic programming algorithm min 1 2 ⁢ ω T ⁢ L ⁢ ⁢ ω + C ⁢ ⁢ Σ ℓ ⁢ ξ ℓ s . t . ∀ ( x a , x b , x c ) ∈ ⁢ ω T ⁡ ( g ⁡ ( x a , x c ) - g ⁡ ( x a , x b ) ) ≥ 1 - ξ ℓ , wherein , ω i ≥ 0 ξ ℓ ≥ 0 L is given by the equation L=(A T A)*(A T A), g( ) is given by the equation g(x,y)=(A T x−A T y)*(A T x−A T y), ω is a set of diagonal elements in W, ω i is a particular diagonal element in W, C is a balance parameter which is automatically adjusted and which is greater than zero, l is an index for the set of relaxed relative comparison constraints for ξ l are slack variables, * denotes an element-wise product between vectors, and A is a prescribed transformation matrix.