9. The process of claim 1 , wherein is given by the equation ={x 1 , x 2 , . . . , x n }, n is a total number of training images in x i is a feature vector for the i-th training image comprising the low-level visual features contained within said image, is given by the equation ={x i (k) } i=1 n k = wherein n k is a number of training images in and the process action of learning a semantic distance function (SDF) f (k) for comprises actions of: generating a set of relaxed relative comparison constraints for wherein is given by the equation ={(x a ,x b ,x c )} and (x a ,x b ,x c ) is a subset of all the possible triples of training images in which satisfies either, a first condition in which the intuitive semantic distance between x a and x c is greater than said distance between x a and x b , or a second condition in which the intuitive semantic distance between x a and x c equals said distance between x a and x b but the difference between the features in x a and the features in x c is greater than the difference between the features in x a and the features in x b ; randomly sampling a prescribed number m of the constraints from resulting in a subset of relaxed relative comparison constraints for given by (i=1, . . . , m); training m different pair-wise SDFs {f 1 (k) , . . . , f m (k) } for wherein each pair-wise SDF f i (k) is trained using and generating f (k) by computing an average of the m different pair-wise SDFs {f 1 (k) , . . . , f m (k) }.
9. The process of claim 1 , wherein is given by the equation ={x 1 , x 2 , . . . , x n }, n is a total number of training images in x i is a feature vector for the i-th training image comprising the low-level visual features contained within said image, is given by the equation ={x i (k) } i=1 n k = wherein n k is a number of training images in and the process action of learning a semantic distance function (SDF) f (k) for comprises actions of: generating a set of relaxed relative comparison constraints for wherein is given by the equation ={(x a ,x b ,x c )} and (x a ,x b ,x c ) is a subset of all the possible triples of training images in which satisfies either, a first condition in which the intuitive semantic distance between x a and x c is greater than said distance between x a and x b , or a second condition in which the intuitive semantic distance between x a and x c equals said distance between x a and x b but the difference between the features in x a and the features in x c is greater than the difference between the features in x a and the features in x b ; randomly sampling a prescribed number m of the constraints from resulting in a subset of relaxed relative comparison constraints for given by (i=1, . . . , m); training m different pair-wise SDFs {f 1 (k) , . . . , f m (k) } for wherein each pair-wise SDF f i (k) is trained using and generating f (k) by computing an average of the m different pair-wise SDFs {f 1 (k) , . . . , f m (k) }. 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.