title: Using graphical models to infer multiple visual classification features.
abstract: This paper describes a new model for human visual classification that enables the recovery of image features that explain performance on different visual classification tasks. Unlike some common methods, this algorithm does not explain performance with a single linear classifier operating on raw image pixels. Instead, it models classification as the result of combining the output of multiple feature detectors. This approach extracts more information about human visual classification than has been previously possible with other methods and provides a foundation for further exploration.
authors.affiliation: Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. mgross@mit.edu
authors.forename: Michael G
authors.initials: MG
authors.lastname: Ross
authors.forename: Andrew L
authors.initials: AL
authors.lastname: Cohen
mesh_terms: D000465:Algorithms; D005556:Form Perception / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D016011:Normal Distribution; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D015203:Reproducibility of Results