For many of the state-of-the-art computer vision algorithms, image segmentation
is an important preprocessing step. As such, several image segmentation algorithms have been proposed, however, with certain reservation due to high computational load and many hand-tuning parameters. Correlation clustering, a graphpartitioning algorithm often used in natural language processing and document
clustering, has the potential to perform better than previously proposed image segmentation algorithms. We improve the basic correlation clustering formulation by
taking into account higher-order cluster relationships. This improves clustering
in the presence of local boundary ambiguities. We first apply the pairwise correlation clustering to image segmentation over a pairwise superpixel graph and
then develop higher-order correlation clustering over a hypergraph that considers higher-order relations among superpixels. Fast inference is possible by linear programming relaxation, and also effective parameter learning framework by
structured support vector machine is possible. Experimental results on various
datasets show that the proposed higher-order correlation clustering outperforms
other state-of-the-art image segmentation algorithms.