Affiliation: | a Department of Psychology, Queens University, Kingston, Ontario, Canada K7L 3N6 b Centre for Machine Intelligence and Robotics, The University of Alberta, Edmonton, AB T6G 2E9, Canada |
Abstract: | In this paper we consider a technique for pattern classification based upon the development of prototypes which capture the distinguishing features (“disjunctive prototypes”) of each pattern class and, via cross-correlation with incoming test images, enable efficient pattern classification. We evaluate such a classification procedure with prototypes based on the images per se (direct code), Gabor scheme (multiple fixed filter representation) and an edge (scale space-based) coding scheme. Our analyses, and comparisons with human pattern classification performance, indicate that the edge-only disjunctive prototypes provide the most discriminating classification performance and are the more representative of human behaviour. |