1. A system for performing classification through generative models of features occurring in an image, comprising: stored category-conditional probability distributions of features with one such category-conditional probability distribution assigned to each of a plurality of image categories; and a classifier, comprising: a feature identifier to assign each of a plurality of training images stored to one of the image categories, to identify features occurring in each training image, to retrieve an unclassified image, and to identify the features occurring in the unclassified image, wherein each of the identified features are represented 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; a likelihood evaluator to evaluate the identified features against the category-conditional probability distributions for each of the image categories; and a categorizer to assign the unclassified image to one image category by maximizing the category-conditional likelihood of the category-conditional probability distribution of the identified features; and a processor to process the unclassified image based on the one image category by converting the unclassified image.
1. A system for performing classification through generative models of features occurring in an image, comprising: stored category-conditional probability distributions of features with one such category-conditional probability distribution assigned to each of a plurality of image categories; and a classifier, comprising: a feature identifier to assign each of a plurality of training images stored to one of the image categories, to identify features occurring in each training image, to retrieve an unclassified image, and to identify the features occurring in the unclassified image, wherein each of the identified features are represented 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; a likelihood evaluator to evaluate the identified features against the category-conditional probability distributions for each of the image categories; and a categorizer to assign the unclassified image to one image category by maximizing the category-conditional likelihood of the category-conditional probability distribution of the identified features; and a processor to process the unclassified image based on the one image category by converting the unclassified image. 11. A system according to claim 1 , wherein the unclassified image is assigned to the category for which the category-conditional likelihoods are highest.