A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

  • Authors:
  • Laurent Itti;Christof Koch;Ernst Niebur

  • Affiliations:
  • California Institute of Technology, Pasadena;California Institute of Technology, Pasadena;Johns Hopkins Univ., Baltimore, MD

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 1998

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Abstract

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.