Iterative image segmentation with feature driven heuristic four-color labeling

Multilabel segmentation is an important research branch in image segmentation field. In our previous work, Multiphase Multiple Piecewise Constant and Geodesic Active Contour (MMPC-GAC) model was proposed, which can effectively describe multiple objects and background with intensity inhomogeneity. It can be approximately solved with Multiple Layer Graph (MLG) methods. To make the optimization more efficient and limit the approximate error, four-color labeling theorem was further introduced which can limit the MLG within three layers (representing four phases). However, the adopted random four-color labeling method usually provides chaotic color maps with obvious inhomogeneity for those semantic consistent regions. For this case, a new and alternative method named heuristic four-color labeling is proposed in this paper, which aims to generate more reasonable color maps with a global view of the whole image. And compared with the random four-color labeling strategy, the whole iterative algorithm based on our method usually produces better segmentations with faster convergence, particularly for images with clutters and complicated structures. This strategy is a good substitute for random coloring method when the latter produces unsatisfactory messy segmentation. Experiments conducted on public dataset demonstrate the effectiveness of the proposed method.