20. A method according to claim 15 , further comprising: determining the category-conditional likelihoods as a product of the category-conditional likelihoods for each of the identified features for the unclassified image.
20. A method according to claim 15 , further comprising: determining the category-conditional likelihoods as a product of the category-conditional likelihoods for each of the identified features for the unclassified image. 24. A method according to claim 20 , further comprising: approximating each likelihood of occurrence p(x n |c) as a latent conditionally independent distribution model expressed by the equation: p ⁡ ( x n | c ) = arg ⁢ max k ∈ K ⁢ ∏ d = 1 D ⁢ p k ⁡ ( x nd | c ) where k is an index to an independent component, 0≦k ≦K; d denotes a dimension index, 1≦d ≦D; x nd denotes a value of the d th dimension of feature x n ; p k (c) denotes a category-conditional probability that a feature is generated from the k th independent component; p k (x n |c) denotes a category-and-latent conditional likelihood that a feature, x n , has x nd as a value of its d th dimension.