The labeling should be consistent, and should favor more probable interpretations if there is more than one option. Consistency means that no two objects of the image appear in an illegal configuration - e.g. an object labeled house in the middle of an object labeled lake will be considered inconsistent in most scenes. Conversely, an object labeled house surrounded by an object labeled lawn in the middle of a lake may be fully acceptable. Two main approaches may be chosen to achieve this goal. Discrete labeling allows only one label to be assigned to each object in the final labeling.
Effort is directed to achieving a consistent labeling all over the image.
Probabilistic labeling allows multiple labels to co-exist in objects. Labels are probabilistically weighted, with a label confidence being assigned to each object label. The main difference is in interpretation robustness. Discrete labeling always finds either a consistent labeling or detects the impossibility of assigning consistent labels to the scene. Often, as a result of imperfect segmentation, discrete labeling fails to find a consistent interpretation even if only a small number of local inconsistencies is detected.