Quantifying the unimportance of prior probabilities in a computer vision problem

An empirical investigation of the importance of accurate assessment of prior probabilities in a typical visual classification problem, handwritten ZIP code recognition, is presented. Prior probabilities for individual digits and entire ZIP codes were investigated; the results for priors of individual digits are summarized. In studies of prior distributions over entire ZIP codes, it was found that the qualitative information had a major effect on the efficacy of the algorithm, whereas quantitative information was relatively unimportant. It is concluded that precise estimation of prior probabilities is unnecessary in the domain of computer vision, whereas accurate qualitative assessment of possibilities is important