Utilizing the hungarian algorithm for improved classification of high-dimension probability density functions in an image recognition problem

Abstract

A method is presented for the classification of images described using high-dimensional probability density functions (pdfs). A pdf is described by a set of n points sampled from its distribution. These points represent feature vectors calculated from windows sampled from an image. A mapping is found, using the Hungarian algorithm, between the set of points describing a class, and the set for a pdf to be classified, such that the distance that points must be moved to change one set into the other is minimized. The method uses these mappings to create a classifier that can model the variation within each class. The method is applied to the problem of classifying plants based on images of their leaves, and is found to outperform several existing methods. © 2012 Springer-Verlag.