Neural Network for Visual Search Classification

Abstract

Visual search describes the process of how the eyes move in a visual field in order to acquire a target. Visual search needs to be quantified to improve future search strategies. This paper describes a hybrid neural network used to perform visual search classification. The neural network consists of a Learning vector quantization network (LVQ) and a single layer perceptron. The objective of this neural network is to classify the various human visual search patterns into predetermined classes. The classes signify the different search strategies used by individuals to scan the same target pattern. The input search patterns are quantified with respect to an ideal search pattern, determined by the user. A supervised learning rule, Learning vector quantization (lvq1) is used to train the network.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410536

Entities

People

  • H. Raju
  • P. A. Wetzel
  • R. S. Hobson

Organizations

  • Virginia Commonwealth University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Biomedical Engineering
  • Classification
  • Computer-Aided Design
  • Electrical Engineering
  • Engineering
  • Eye Movements
  • Learning
  • Machine Learning
  • Markov Chains
  • Military Research
  • Neural Networks
  • Probability
  • Supervised Machine Learning
  • Template Patterns
  • Training

Fields of Study

  • Computer science

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks