28. A method according to claim 15 , further comprising: estimating the category-conditional probability distributions from training images that are each assigned to one or more of the categories.
15. A method for performing classification through generative models of features occurring in an image, comprising: assigning each of a plurality of training images to one of a plurality of image categories; identifying features occurring in each training image; determining category-conditional probabilities of the features for each of the training images for their respective image categories; maintaining the category-conditional probability distributions of features with one such category-conditional probability distribution assigned to each of the image categories; retrieving an unclassified image and identifying the features occurring in the unclassified image; representing each of the identified features as an element in a feature list having a variable length, each element comprising a value in a chosen space of features that comprises one or more dimensions and the value comprising measurements along each of the dimensions being either continuous or discrete valued; evaluating the identified features against the category-conditional probability distributions for each of the image categories; assigning the unclassified image to one image category by maximizing the category-conditional likelihood of the category-conditional probability distribution of the identified features; and processing the unclassified image based on the one image category by converting the unclassified image.