A model of visual recognition and categorization

Shimon Edelman, Sharon Duvdevani-Bar

Research output: Contribution to journalReview articlepeer-review

69 Scopus citations

Abstract

To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. Developments in computer vision suggest that it may be possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Daily life situations, however, typically require categorization, rather than recognition, of objects. Due to the open-ended character of both natural and artificial categories, categorization cannot rely on interpolation between stored examples. Nonetheless, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes appears to be computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies.

Original languageEnglish
Pages (from-to)1191-1202
Number of pages12
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Volume352
Issue number1358
DOIs
StatePublished - 29 Aug 1997
Externally publishedYes

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