Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts
This work provides an incremental improvement for computer vision and graphics researchers by offering a more efficient formulation for decomposition tasks.
The authors tackled the image and mesh decomposition problems by proposing a lifted multicut problem (LMP) with long-range terms to address limitations of the standard multicut problem, achieving competitive results with state-of-the-art decompositions on the BSDS-500 and Princeton benchmarks.
Formulations of the Image Decomposition Problem as a Multicut Problem (MP) w.r.t. a superpixel graph have received considerable attention. In contrast, instances of the MP w.r.t. a pixel grid graph have received little attention, firstly, because the MP is NP-hard and instances w.r.t. a pixel grid graph are hard to solve in practice, and, secondly, due to the lack of long-range terms in the objective function of the MP. We propose a generalization of the MP with long-range terms (LMP). We design and implement two efficient algorithms (primal feasible heuristics) for the MP and LMP which allow us to study instances of both problems w.r.t. the pixel grid graphs of the images in the BSDS-500 benchmark. The decompositions we obtain do not differ significantly from the state of the art, suggesting that the LMP is a competitive formulation of the Image Decomposition Problem. To demonstrate the generality of the LMP, we apply it also to the Mesh Decomposition Problem posed by the Princeton benchmark, obtaining state-of-the-art decompositions.