A likelihood ratio classifier for histogram features

In a number of classification problems, the features are represented by histograms. Traditionally, histograms are compared by relatively simple distance measures such as the chi-square, the Kullback-Leibler, or the Euclidean distance. This paper proposes a likelihood ratio classifier for histogram features that is optimal in Neyman-Pearson sense. It is based on the assumptions that histograms can be modelled by a multinomial distribution and the bin probabilities of the histograms by a Dirichlet probability density. A simple method to estimate the Dirichlet parameters is included. Feature selection prior to classification improves the classification performance. Classification results are presented on periocular and face data from various datasets. It is shown that the proposed classifier out-performs the chi-square distance measure.