13. The method according to claim 12 , in which P ℑ (ƒ i |z k ) is determined by maximization of the log-likelihood function: log ⁢ ∏ i = 1 N ⁢ ⁢ P ⁡ ( f i ❘ Z ) u i = ∑ i = 1 N ⁢ u i ⁢ log ⁡ ( ∑ k = 1 K ⁢ P z ⁡ ( z k ) ⁢ p ⁡ ( f i ❘ z k ) ) where u i is the number of annotation words for image ƒ i , and P z (z k ) and P V (w j |z k ) are determined by maximization of the log-likelihood function: = log ⁢ ⁢ P ⁡ ( F , V ) = ∑ i = 1 N ⁢ ∑ j = 1 M ⁢ n ⁡ ( w i j ) ⁢ log ⁢ ⁢ P ⁡ ( f i , w j ) where n (w i j ) denotes the weight of annotation word w j , i.e., occurrence frequency, for image ƒ i ; and the model is resolved by applying the expectation-maximization (EM) technique, comprising: (i) an expectation (E) step where the posterior probabilities are computed for the hidden variable z k based on the current estimates of the parameters; and (ii) an maximization (M) step, where parameters are updated to maximize the expectation of the complete-data likelihood log P (F,V,Z) given the posterior probabilities computed in the preceding E-step, whereby the probabilities can be iteratively determined by fitting the model to the training image database and the associated annotations.
18. The Bayesian model according to claim 13 , wherein images are retrieved for word queries by determining the conditional probability P(ƒ i |w j ) P ⁢ ( f i ❘ w j ) = ∫ P ⁡ ( f i ❘ z ) ⁢ P ⁡ ( z ❘ w j ) ⁢ ⅆ z = ∫ P V ⁡ ( w j ❘ z ) ⁢ p ⁡ ( f i ❘ z ) ⁢ P ⁡ ( z ) P ⁡ ( w j ) ⁢ ⅆ z = E z ⁢ { P V ⁡ ( w j ❘ z ) ⁢ p ⁡ ( f i ❘ z ) P ⁡ ( w j ) } in which the expectation is estimated as follows: P ⁡ ( f i ❘ w j ) ≈ ∑ k = 1 K ⁢ P V ⁡ ( w j ❘ z k ) ⁢ p ⁡ ( f i ❘ z k ) ∑ h = 1 K ⁢ P V ⁡ ( w j ❘ z h ) = ∑ k = 1 K ⁢ p ⁡ ( f i ❘ z k ) ⁢ y k where ⁢ ⁢ y k = P V ⁡ ( w j ❘ z k ) ∑ h ⁢ P V ⁡ ( w j ❘ z h ) , and wherein the images in the database with the top highest P(ƒ i |w j ) are returned as the retrieval result for each query word.