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Excerpt from A Brief Survey of Knowledge Aggregation Methods
Figure 2. Illustration of a labeling problem. There are four objects, corresponding to the four image segments. Each object can have one of four possible labels. In this example, the possible labels are House, Grass, Trees, or Sky. The state of the procedure is defined by a probability distribution over the possible labels at each object. In the figure, at the current time we are 55% sure that Object 1 is the Sky.
For any iterative knowledge aggregation method, the intent is that the probabilities converge to 1 for the correct labels at each object.
Finally, the current state and evidence are combined, resulting in a new state. This new state is again a probability distribution over the set of labels at each object. After a number of iterations of the procedure, the intent is that the process will converge to a state with only one label at each object having a high probability.
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