title: Superordinate shape classification using natural shape statistics.
abstract: This paper investigates the classification of shapes into broad natural categories such as animal or leaf. We asked whether such coarse classifications can be achieved by a simple statistical classification of the shape skeleton. We surveyed databases of natural shapes, extracting shape skeletons and tabulating their parameters within each class, seeking shape statistics that effectively discriminated the classes. We conducted two experiments in which human subjects were asked to classify novel shapes into the same natural classes. We compared subjects' classifications to those of a naive Bayesian classifier based on the natural shape statistics, and found good agreement. We conclude that human superordinate shape classifications can be well understood as involving a simple statistical classification of the shape skeleton that has been "tuned" to the natural statistics of shape.
authors: Department of Psychology, Center for Cognitive Science, Rutgers University, New Brunswick, United States. jdwilder@ruccs.rutgers.edu
authors: John
authors: J
authors: Wilder
authors: Jacob
authors: J
authors: Feldman
authors: Manish
authors: M
authors: Singh
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D007802:Language; D008297:Male; D008959:Models, Neurological; D010775:Photic Stimulation; D055815:Young Adult