On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision

Abstract

Models of object recognition in cortex have so far been mostly applied to tasks involving the recognition of isolated objects presented on blank backgrounds. However, ultimately models of the visual system have to prove themselves in real world object recognition tasks. Here we took a first step in this direction: We investigated the performance of the HMAX model of object recognition in cortex recently presented by Riesenhuber & Poggio on the task of face detection using natural images. We found that the standard version of HMAX performs rather poorly on this task, due to the low specificity of the hardwired feature set of C2 units in the model (corresponding to neurons in intermediate visual area V4) that do not show any particular tuning for faces vs. background. We show how visual features of intermediate complexity can be learned in HMAX using a simple learning rule. Using this rule, HMAX outperforms a classical machine vision face detection system presented in the literature. This suggests an important role for the set of features in intermediate visual areas in object recognition.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA454940

Entities

People

  • Jennifer Louie
  • Maximilian Riesenhuber
  • Thomas Serre
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cognitive Science
  • Computer Vision
  • Data Sets
  • Detection
  • Identification
  • Machine Learning
  • Machines
  • Object Recognition
  • Recognition
  • Self Organizing Systems
  • Standards
  • Test Sets
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.