Asymmetric Cuts: Joint Image Labeling and Partitioning

For image segmentation, recent advances in optimization make it possible to combine noisy region appearance terms with pairwise terms which can not only discourage, but also encourage label transitions, depending on boundary evidence. These models have the potential to overcome prob-lems such as the shrinking bias. However, with the ability to encourage label transitions comes a different problem: strong boundary evidence can overrule weak region appearance terms to create new regions out of nowhere. While some label classes exhibit strong internal boundaries, such as the background class which is the pool of objects. Other label classes, meanwhile, should be modeled as a single region, even if some internal boundaries are visible. We therefore propose in this work to treat label classes asymmetrically: for some classes, we allow a further partitioning into their constituent objects as supported by boundary evidence; for other classes, further partitioning is forbidden. In our experiments, we show where such a model can be useful for both 2D and 3D segmentation