Adaptive video image recognition system using a committee machine

An ensemble of classifiers has been built to solve the problem of video image recognition. The paper offers a way to estimate the a posteriori probability of an image belonging to a particular class in the case of an arbitrary distance and nearest neighbor method. The estimation is shown to be equivalent to the optimal naive Bayesian estimate given the Kullback-Leibler discrimination being used as similarity measure. The block diagram of a video image recognition system is presented. The system features automatic adaptation of the list of images of identical objects which is fed to the committee machine input. The system is tested in face recognition task using popular data sets (FERET, AT&T, Yale) and the results are discussed.