1. A computer-implemented method of processing an image having at least one high-level attribute, the method comprising: accessing at least one random decision forest arranged to take into account the high-level attribute by virtue of having been trained using training images for which values of at least one global variable expressing the high-level attribute are known; for each of a plurality of image elements of the image, pushing the image element through the random decision forest to obtain candidate output values and, information about the certainty of the candidate output values; using the candidate output values and certainty information to select a set of candidate output values as either: a most confident set of candidate output values over the global variable and the candidate output values jointly; or a most confident set of candidate output values given an estimate of the global variable; or a weighted combination of the candidate output values given an estimate of the global variable; the method further comprising: receiving the image as part of a stream of images, calculating a running average value of the global variable of the image and using the calculated value to influence how the candidate output values are obtained, receiving subsequent images from the stream of images and repeating the calculating and influencing for each subsequent image; or receiving a sequence of images comprising the image and wherein receiving the random decision forest comprises receiving a single random decision forest trained to jointly estimate the global variable and the candidate output values over all images in the sequence.