6. The method of claim 1 , wherein the statistical significance of a feature is determined by a comparison of the coefficients values associated with the feature and standard deviation of the coefficients values.
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, 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; 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.