Maximum Likelihood — formal statement.

• Assumption: data for each class is normally distributed (Gaussian) in feature space.
• Let C = (C₁, C₂, …, C_nc) be the set of nc classes.
• For a pixel with gray-level vector x, compute posterior probability P(Cᵢ | x) for every class.
• Assign the pixel to Cᵢ if P(Cᵢ | x) ≥ P(Cⱼ | x) for all j ≠ i — i.e., the class with the highest posterior wins.
• Reference: Gong lecture notes, UC Berkeley (nature.berkeley.edu).