Abstract

Pairwise Markov random fields are an effective framework for solving many pixel labeling problems in computer vision. However, their performance is limited by their inability to capture higher-order correlations. Recently proposed higher-order models are showing supe- rior performance to their pairwise counterparts. In this paper, we derive two variants of the higher-order lower linear envelop model and show how to perform tractable move-making inference in these models. We propose a novel use of this model for encoding consistency constraints over large sets of pixels. Importantly these pixel sets do not need to be contiguous. However, the consistency model has a large number of parameters to be tuned for good performance. We exploit the structured SVM paradigm to learn optimal parameters and show some practical techniques to over- come huge computation requirements. We evaluate our model on the problems of image denoising and semantic segmentation.