Compound Sequential Probability Ratio Test for the Classification of Statistically Dependent Patterns

A sequential test procedure for the classification of statistically dependent patterns is developed. The test is based on the optimum (Bayes) compound decision theory and the theory of Wald´s sequential probability ratio test (SPRT). The compound sequential probability ratio (SPRT) is shown to be recursively computable at every instant of the decision process. A two-class recognition problem with first-order Markov dependence among the pattern classes is considered for the purpose of comparing the performance of the CSPRT with that of Wald´s SPRT. It is shown that when the pattern classes are statistically dependent the CSPRT requires, on the average, fewer features per pattern than Wald´s equally reliable SPRT. Finally, the results of computer simulated recognition experiments using CSPRT and other sequential and nonsequential decision schemes are discussed in detail.