The Kinect provides an opportunity to collect large quantities of training data for visual learning algorithms relatively effortlessly. To this end we investigate learning to automatically segment humans from cluttered images (without depth information) given a bounding box. For this algorithm obtaining a large dataset of images with segmented humans is crucial as it enables the possible variations in human appearances and backgrounds to be learnt. We show that a large dataset of roughly 3400 humans can be automatically acquired very cheaply using the Kinect. Segmenting humans is then cast as a learning problem with linear classifiers trained to predict segmentation masks from sparsely coded local HOG descriptors. These classifiers introduce top-down knowledge to obtain a crude segmentation of the human which is then refined using bottom up information from local color models in a Snap-Cut  like fashion. The method is quantitatively evaluated on images of humans in cluttered scenes and a high performance obtained (88:5% overlap score). We also show that the method can be completely automated-segmenting humans given only the images without requiring a bounding box and compare with a previous state of the art method.