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A Biologically Plausible Approach to Cat and Dog Discrimination

  • Bruce A. Draper
  • Kyungim Baek
  • Jeff Boody
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

The paper describes a computational model of human expert object recognition in terms of pattern recognition algorithms. In particular, we model the process by which people quickly recognize familiar objects seen from familiar viewpoints at both the instance and category level. We propose a sequence of unsupervised pattern recognition algorithms that is consistent with all known biological data. It combines the standard Gabor-filter model of early vision with a novel cluster-based local linear projection model of expert object recognition in the ventral visual stream. This model is shown to be better than standard algorithms at distinguishing between cats and dogs.

Keywords

Visual Memory Inferior Frontal Gyrus Independent Component Analysis Gabor Filter Familiar Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Bruce A. Draper
    • 1
  • Kyungim Baek
    • 1
  • Jeff Boody
    • 1
  1. 1.Department of Computer ScienceColorado State UniversityFort CollinsUSA

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