13. The process of claim 1 , wherein the process action of probabilistically propagating the vector of keyword annotations for each training image in to the new image comprises actions of: utilizing the ranking list for to rank the vectors of keyword annotations for all the training images in resulting in a set of ranked keyword annotations for given by {t 1 (k) , t 2 (k) , . . . , t n k (k) }, wherein n k is a total number of training images in and t i (k) is the vector of keyword annotations for the i-th training image in the ranking list; utilizing the ranking list for to rank the pair-wise feature-based semantic distance scores between the new image and each training image in resulting in a set of ranked pair-wise feature-based semantic distance scores for given by {d 1 (k) , d 2 (k) , . . . , d n k (k) }, wherein d i (k) is the pair-wise feature-based semantic distance score between the new image and the i-th training image in the ranking list; computing the cluster-specific vector w (k) of probabilistic annotations as w ( k ) = 1 n k ⁢ ∑ i ⁢ ⅆ 1 ( k ) ⁢ - α ( k ) ⅆ i ( k ) ⁢ - α ( k ) * t i ( k ) , wherein α (k) is a prescribed coefficient; and normalizing w (k) .
15. The process of claim 13 , wherein w (k) is normalized such that the L−1 norm of w (k) is one.