9. The method according to claim 8 , in which (f i |z k ) is determined by maximization of the log-likelihood function: log ⁢ ∏ i = 1 N ⁢ ⁢ ( f i ❘ Z ) u i = ∑ i = 1 N ⁢ u i ⁢ log ⁡ ( ∑ k = 1 K ⁢ P z ⁡ ( z k ) ⁢ ⁢ ( f i ❘ z k ) ) where u i is the number of annotation words for image f 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 f 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.
9. The method according to claim 8 , in which (f i |z k ) is determined by maximization of the log-likelihood function: log ⁢ ∏ i = 1 N ⁢ ⁢ ( f i ❘ Z ) u i = ∑ i = 1 N ⁢ u i ⁢ log ⁡ ( ∑ k = 1 K ⁢ P z ⁡ ( z k ) ⁢ ⁢ ( f i ❘ z k ) ) where u i is the number of annotation words for image f 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 f 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. 11. The method according to claim 9 , wherein the expectation of the likelihood log P(F,Z) for the estimated P(Z|F) is expressed as: ∑ k = 1 K ⁢ ∑ i = 1 N ⁢ log ⁡ ( P z ⁡ ( z k ) ⁢ ⁢ ( f i ❘ z k ) ) ⁢ ⁡ ( z k ❘ f i ) and the expectation of log P(F,V,Z) and log P(F|V,Z) are maximized with Lagrange multipliers to P z (z l ), p ℑ (f u |z l ) and P V (w v |z l ), under the normalization constraints ∑ k = 1 K ⁢ P z ⁡ ( z k ) = 1 , ∑ k = 1 K ⁢ P ⁡ ( z k ❘ f i , w j ) = 1 for any f i , w j , and z l , the parameters being determined as μ k = ⁢ ∑ i = 1 N ⁢ u i ⁢ f i ⁢ p ⁡ ( z k ❘ f i ) ∑ s = 1 N ⁢ u s ⁢ p ⁡ ( z k ❘ f s ) ∑ k ⁢ = ⁢ ∑ i = 1 N ⁢ u i ⁢ p ⁡ ( z k ❘ f i ) ⁢ ( f i ❘ μ k ) ⁢ ( f i - μ k ) T ∑ s = 1 N ⁢ u s ⁢ p ⁡ ( z k ❘ f s ) P z ⁡ ( z k ) = ⁢ ∑ j = 1 M ⁢ ∑ i = 1 N ⁢ u ⁡ ( w i j ) ⁢ P ⁡ ( z k ❘ f i , w j ) ∑ j = 1 M ⁢ ∑ i = 1 N ⁢ n ⁡ ( w i j ) P V ⁡ ( w j ❘ z k ) = ⁢ ∑ i = 1 N ⁢ n ⁡ ( w i j ) ⁢ P ⁡ ( z k ❘ f i , w j ) ∑ u = 1 M ⁢ ∑ V = 1 N ⁢ n ⁡ ( w V u ) ⁢ P ⁡ ( z k ❘ f V , w u ) ; the number of concepts, K, is chosen to maximize log ⁡ ( P ⁡ ( F , V ) ) - m K 2 ⁢ log ⁡ ( MN ) where m K is the number of free parameters needed for a model with K mixture components; and 
 m K =( K− 1)+ K ( M− 1)+ K ( N− 1)+ L 2 =K ( M+N− 1)+ L 2 −1; a joint distribution is used to model the probability of an event that a word w j belonging to semantic concept z k is an annotation word of image f i : 
 P ( w j ,z k ,f i )= P z ( Z k ) p ℑ ( f i |z k ) P V ( w j |z k ) or P ⁡ ( w j ❘ f i ) = ⁢ ∫ P v ⁡ ( w j ❘ z ) ⁢ p ⁡ ( z ❘ f i ) ⁢ ⅆ z = ⁢ ∫ P V ⁡ ( w j ❘ z ) ⁢ ⁢ ( f i ❘ z ) ⁢ P ⁡ ( z ) p ⁡ ( f i ) ⁢ ⅆ z = ⁢ E z ⁢ { P v ⁡ ( w j ❘ z ) ⁢ ⁢ ( f i ❘ z ) p ⁡ ( f i ) } where 
 p ( f i )=∫ ( f i |z ) P z ( z ) dz=E z {p ℑ ( f i |z )}, and E z {●} denotes the expectation over P(z k ); and a Monte Carlo integration is used to derive P ⁡ ( w j ❘ f i ) ≈ ⁢ ∑ k = 1 K ⁢ P V ⁡ ( w j ❘ z k ) ⁢ ⁢ ( f i ❘ z k ) ∑ h = 1 K ⁢ ⁢ ( f i ❘ z h ) = ⁢ ∑ k = 1 K ⁢ P V ⁡ ( w j ❘ z k ) ⁢ x k where ⁢ ⁢ x k = ⁢ ⁢ ( f i ❘ z k ) ∑ h = 1 K ⁢ ⁢ ( f i ❘ z h ) and wherein the words with the top highest P(w j |f i ) are returned to annotate the image.