1. A method of retrieving media, comprising: (a) defining a probabilistic framework organizing media with respect to concepts represented in the respective media comprising at least one hidden layer; (b) automatically mapping elements of a set of media to the probabilistic framework, using at least one processor, based on implicit concepts represented within each element of the set of media, the probabilistic framework comprising at least one joint probability distribution which models a probability that a symbol belonging to a respective concept is an annotation symbol of respective media; (c) automatically determining, using at least one processor, at least one implicit concept represented in a received query; (d) automatically determining, using at least one processor, a probabilistic correspondence of elements of the set of media with the determined at least one implicit concept; and (e) outputting at least one representation or identifier of at least one element of the set of media selectively in dependence on at least the determined probabilistic correspondence.
6. The method according to claim 1 , wherein the query comprises an image, and said automatically determining comprises associating words with the image using a Bayesian model comprising a hidden concept layer which connects a visual feature layer and a word layer, which is discovered by fitting a generative model to a training set comprising images and annotation words, wherein the conditional probabilities of the visual features and the annotation words given a hidden concept class are determined based on an Expectation-Maximization (EM) based iterative learning procedure.