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A Biological Model of Object Recognition with Feature Learning

Author(s)
Louie, Jennifer
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Abstract
Previous biological models of object recognition in cortex have been evaluated using idealized scenes and have hard-coded features, such as the HMAX model by Riesenhuber and Poggio [10]. Because HMAX uses the same set of features for all object classes, it does not perform well in the task of detecting a target object in clutter. This thesis presents a new model that integrates learning of object-specific features with the HMAX. The new model performs better than the standard HMAX and comparably to a computer vision system on face detection. Results from experimenting with unsupervised learning of features and the use of a biologically-plausible classifier are presented.
Date issued
2003-06-01
URI
http://hdl.handle.net/1721.1/5571
Other identifiers
AITR-2003-009
CBCL-227
Series/Report no.
AITR-2003-009CBCL-227
Keywords
AI

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  • AI Technical Reports (1964 - 2004)
  • CBCL Memos (1993 - 2004)

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