A Biological Model of Object Recognition with Feature Learning

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. 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.

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Document Details

Document Type
Technical Report
Publication Date
May 21, 2003
Accession Number
ADA459618

Entities

People

  • Jennifer Louie

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Computational Neuroscience
  • Computational Science
  • Computer Science
  • Computer Vision
  • Detectors
  • Electrical Engineering
  • Kernel Functions
  • Machine Learning
  • Neural Pathways
  • Object Recognition
  • Recognition
  • Supervised Machine Learning
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks