Classification of sign-based image representations based on distance functions

Abstract

The paper proposes various approaches to classifying sign-based representations of images based on distance functions. Any image is represented as a set of features describing differences in brightness. The construction of a distance function is proposed using classical functionals of information theory: the Shannon entropy and the Kullback-Leibler distance. It is shown that the Bayes classification in the case of independent features can be also described by distance functions. In the last section, the proposed approaches are evaluated using a face detection problem.