Image Recognition Algorithms Based on the Representation of Classes by Convex Hulls

摘要

Various approaches to the construction of pattern recognition algorithms based on the representation of classes as convex hulls in a multidimensional feature space are considered. This trend is well suited for biometrics problems with a large number of classes and small volumes of learning samples by class, for example, for problems of recognizing people by faces or fingerprints. In addition to simple algorithms for a point hitting a convex hull, algorithms of the nearest convex hull with different approaches to assessing the proximity of a test point to the convex hull of classes are investigated. Comparative experimental results are given and the advantages and disadvantages of the proposed approach are formulated.