1. A computer implemented method for learning a random multinomial logit (RML) classifier for scene segmentation, the method comprising: receiving 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; generating 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; selecting one or more texture-layout features from the plurality of the texton images; learning multiple multinomial logistic regression models of the RML classifier based on the selected texture-layout features; and evaluating the performance of the multiple multinomial logistic regression models based on the semantic labels of the objects in the image training set.