LAUSR

Image matting is evolving for a wide range of applications including image/video editing. Sampling-based image matting aims to estimate the opacity of foreground objects by properly selecting a pair of foreground and background pixels for every unknown pixel. Sampling-based image matting is essentially an uncertain multicriteria optimization problem (UMCOP). It shows unique advantages in parallelization and handling spatially disconnected regions. However, sampling-based approaches encounter difficulty in accurately evaluating pixel pairs and efficiently optimizing the large-scale UMCOP. To address these two problems, a fuzzy multicriteria evaluation (FMCE) and a multiobjective evolutionary algorithm based on multicriteria decomposition (MOEA-MCD) are proposed. We model three fuzzy membership functions for three selection criteria and aggregate them by Einstein and averaging operators providing FMCE for pixel pairs. MOEA-MCD uses the heuristic information for each criterion by multicriteria decomposition that divides the single objective into multiple objectives and optimizes them simultaneously using a multiobjective optimizer with neighborhood grouping strategy. Experimental results show that FMCE accurately evaluates pixel pairs even in uncertain cases with low satisfaction degree of some evaluation criteria, and the heuristic information for each criterion enhances the population diversity of MOEA-MCD. MOEA-MCD outperforms state-of-the-art large-scale optimization approaches and sampling-based image matting approaches.