22. The system of claim 14 , wherein the RML classifier is further configured to re-learn a multinomial logistic regression model of the RML classifier responsive to the performance of the multinomial logistic regression model being below a performance threshold.
14. A computer system for method for learning a random multinomial logit (RML) classifier for scene segmentation, the system comprising: an image textonization module configured to: receive an image training set, wherein the image training set comprises multiple digital representations of images, and an object of an image of the image training set has a semantic label; and generate a plurality of texton images corresponding to the images in the image training set, wherein a texton image of its corresponding image in the image training set is an image of pixels, and wherein each pixel value in a texton image is replaced by a representation of the pixel value of its corresponding image in the image training set; a feature selection module configured to select one or more texture-layout features from the plurality of the texton images, wherein selecting one or more texture-layout features comprises swapping a feature currently being used by the RML classifier with a randomly selected new feature based on the statistical significance of the feature currently being used; and a RML classifier configured to: learn multiple multinomial logistic regression models of the RML classifier based on the selected texture-layout features; and evaluate the performance of the multiple multinomial logistic regression models based on the semantic labels of the objects in the image training set.