Multiobjective Evolutionary Optimization Based on Fuzzy Multicriteria Evaluation and Decomposition for Image Matting

Pyramid Matting: A Resource-Adaptive Multi-Scale Pixel Pair Optimization Framework for Image Matting

“…Three representative cases were considered in the experiments, which correspond to the cases where image matting approaches can be run for hours, minutes and seconds in a workstation computer, respectively. In case one, the maximum number of PPE/UP was set to 5000 (which is consistent with the setting in [10]) to evaluate the performance of PMF with sufficient computing resources. The maximum number of PPE/UP was set to 50 in case two, which corresponds to the case where image matting approaches are required to provide an alpha matte within minutes on a workstation computer.…”

“…The pixel-pair-optimization-based approach is one of the competitive image matting approaches that have distinct advantages in parallelization [10] and in handling a mislabeled trimap [7] or spatially disconnected foreground [11]- [13]. In this approach, natural image matting problem is modeled as a pixel pair optimization (PPO) problem that can be written as: min g p (x p ) s.t.…”

“…On the other hand, it cannot utilize all of available computing resources when the computing resources are sufficient. Moreover, the issue of missing true sample cannot be completely eliminated in sampling-based approaches due to the complexity of pixel pair optimization [10].…”

“…Recently, several researchers [10], [23]- [25] demonstrated that pixel pairs can be optimized without sampling by using the evolutionary algorithm (a population-based metaheuristic optimization algorithm [26]), which can make use of all available computing resources by adjusting the iteration number of the evolutionary algorithm, and would in theory eliminate the risk of true sample omission. In these approaches (which are known as evolutionary-optimizationbased approaches), all PPO problems involved in an image are solved simultaneously by combining them into one large-scale PPO problem, as illustrated in Eq.…”

“…In their work, a cooperative coevolution operator was embedded into differential evolution algorithm to group unknown pixels with similar colors and optimize the groups separately. In our previous study [10], we pointed out that each of the evaluation criteria involved in pixel pair evaluation can provide heuristic information for PPO. In our previous work, the heuristic information was used to support the optimization by transforming PPO problem into a multi-objective problem and solving it via a multi-objective evolutionary algorithm.…”