3. The method according to claim 2 , in which the visual feature and word distribution is treated as a randomized data generation process, wherein a probability of a concept is represented as P z (z k ); a visual feature is selected f i εF with probability P ℑ (f i |z k ); and a textual word is selected w j εV with probability P V (w j |z k ), from which an observed pair (f i ,w j ) is obtained, such that a joint probability model is expressed as follows: P ⁡ ( f i , w j ) = ⁢ P ⁡ ( w j ) ⁢ P ⁡ ( f i ❘ w j ) = ⁢ P ⁡ ( w j ) ⁢ ∑ k = 1 K ⁢ ⁢ P ⁡ ( f i ❘ z k ) ⁢ P ⁡ ( z k ❘ w j ) = ⁢ ∑ k = 1 K ⁢ ⁢ P z ⁡ ( z k ) ⁢ P ⁡ ( f i ❘ z k ) ⁢ P V ⁡ ( w j ❘ z k ) , and the visual features are generated from K Gaussian distributions, each one corresponding to a z k , such that for a specific semantic concept variable z k , the conditional probability density function of visual feature f i is expressed as: P ⁡ ( f i ❘ z k ) = 1 2 ⁢ π L / 2 ❘ ∑ k 1 / 2 ⁢ ⁢ ⅇ - 1 2 ⁢ ( f i - μ k ) T ⁢ ∑ k - 1 ⁢ ⁢ ( f i - μ k ) where Σ k and μ k are the covariance matrix and mean of visual features belonging to z k , respectively.
3. The method according to claim 2 , in which the visual feature and word distribution is treated as a randomized data generation process, wherein a probability of a concept is represented as P z (z k ); a visual feature is selected f i εF with probability P ℑ (f i |z k ); and a textual word is selected w j εV with probability P V (w j |z k ), from which an observed pair (f i ,w j ) is obtained, such that a joint probability model is expressed as follows: P ⁡ ( f i , w j ) = ⁢ P ⁡ ( w j ) ⁢ P ⁡ ( f i ❘ w j ) = ⁢ P ⁡ ( w j ) ⁢ ∑ k = 1 K ⁢ ⁢ P ⁡ ( f i ❘ z k ) ⁢ P ⁡ ( z k ❘ w j ) = ⁢ ∑ k = 1 K ⁢ ⁢ P z ⁡ ( z k ) ⁢ P ⁡ ( f i ❘ z k ) ⁢ P V ⁡ ( w j ❘ z k ) , and the visual features are generated from K Gaussian distributions, each one corresponding to a z k , such that for a specific semantic concept variable z k , the conditional probability density function of visual feature f i is expressed as: P ⁡ ( f i ❘ z k ) = 1 2 ⁢ π L / 2 ❘ ∑ k 1 / 2 ⁢ ⁢ ⅇ - 1 2 ⁢ ( f i - μ k ) T ⁢ ∑ k - 1 ⁢ ⁢ ( f i - μ k ) where Σ k and μ k are the covariance matrix and mean of visual features belonging to z k , respectively. 4. The method according to claim 3 , in which word concept conditional probabilities P V (●|Z), i.e., P V (w j |z k ) for kε[1,K], are estimated through fitting the probabilistic model to the training set.