Consistent image analogies using semi-supervised learning

In this paper we study the following problem: given two source images A and A′, and a target image B, can we learn to synthesize a new image B′ which relates to B in the same way that A′ relates to A′ We propose an algorithm which a) uses a semi-supervised component to exploit the fact that the target image B is available apriori, b) uses inference on a Markov Random Field (MRF) to ensure global consistency, and c) uses image quilting to ensure local consistency. Our algorithm can also deal with the case when A is only partially labeled, that is, only small parts of A′ are available for training. Empirical evaluation shows that our algorithm consistently produces visually pleasing results, outperforming the state of the art.