Semantically describing the contents of images is one of the classical problems of computer vision. With huge numbers of images being made available daily, there is increasing interest in methods for semantic pixel labelling that exploit large image sets. Graph transduction provides a framework for the flexible inclusion of labeled data that can be exploited in the classification of unlabeled samples without requiring a trained classifier. Unfortunately, current approaches lack the scalability to tackle the joint segmentation of large image sets. Here we introduce an efficient flexible graph transduction approach to semantic segmentation that allows simple and efficient leveraging of large image sets without requiring separate computation of unary potentials, or a trained classifier. We demonstrate that this technique can handle far larger graphs than previous methods, and that results continue to improve as more labeled images are made available. Furthermore, we show that the method is able to benefit from dense or sparse unary labels when they are available.