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 [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.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2003
Accession Number
ADA455936

Entities

People

  • Jennifer Louie

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

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

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • AI & ML