Peters, Robert J. and Gabbiani, Fabrizio and Koch, Christof (2003) Human visual object categorization can be described by models with low memory capacity. Vision Research, 43 (21). pp. 2265-2280. ISSN 0042-6989. http://resolver.caltech.edu/CaltechAUTHORS:20130816-103220181
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Studies of high-level models of visual object categorization have left unresolved issues of neurobiological relevance, including how features are extracted from the image and the role played by memory capacity in categorization performance. We compared the ability of a comprehensive set of models to match the categorization performance of human observers while explicitly accounting for the models’ numbers of free parameters. The most successful models did not require a large memory capacity, suggesting that a sparse, abstracted representation of category properties may underlie categorization performance. This type of representation––different from classical prototype abstraction––could also be extracted directly from two-dimensional images via a biologically plausible early-vision model, rather than relying on experimenter-imposed features.
|Additional Information:||Received 31 October 2001; received in revised form 20 August 2002. c2003 Elsevier Ltd. This work was supported by a Predoctoral Fellowship from the Howard Hughes Medical Institute to R.J. Peters. Additional support was provided by the Engineering Research Centers Program of the National Science Foundation under Award Number EEC-9402726, by the NIMH and by the W.M. Keck Foundation Fund for Discovery in Basic Medical Research at Caltech.|
|Group:||Koch Laboratory, KLAB|
|Subject Keywords:||Visual object categorization; Exemplar model; Multidimensional scaling; Similarity; Representation; Pairs; Triads|
|Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||KLAB Import|
|Deposited On:||11 Jan 2008 23:44|
|Last Modified:||16 Sep 2013 18:28|
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