article · 2003

Feature Learning

Jennifer Louie, Jennifer Louie, Jennifer Louie

Cited by 0Abstract and details only

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.

Cite this paper

APA 7

Louie, J., Louie, J., & Louie, J. (2003). Feature Learning.

MLA 9

Louie, Jennifer, et al. “Feature Learning.” 2003.

Chicago

Louie, Jennifer, Jennifer Louie, and Jennifer Louie. 2003. “Feature Learning.”

Harvard

Louie, J., Louie, J. and Louie, J. (2003) ‘Feature Learning’.

BibTeX

@article{louie2003feature,
  title = {{Feature Learning}},
  author = {Louie, Jennifer and Louie, Jennifer and Louie, Jennifer},
  year = {2003}
}

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