A model of proto-object based saliency

Alexander F. Russell, Stefan Mihalaş, Rudiger von der Heydt, Ernst Niebur, Ralph Etienne-Cummings

Research output: Contribution to journalArticlepeer-review

Abstract

Organisms use the process of selective attention to optimally allocate their computational resources to the instantaneously most relevant subsets of a visual scene, ensuring that they can parse the scene in real time. Many models of bottom-up attentional selection assume that elementary image features, like intensity, color and orientation, attract attention. Gestalt psychologists, however, argue that humans perceive whole objects before they analyze individual features. This is supported by recent psychophysical studies that show that objects predict eye-fixations better than features. In this report we present a neurally inspired algorithm of object based, bottom-up attention. The model rivals the performance of state of the art non-biologically plausible feature based algorithms (and outperforms biologically plausible feature based algorithms) in its ability to predict perceptual saliency (eye fixations and subjective interest points) in natural scenes. The model achieves this by computing saliency as a function of proto-objects that establish the perceptual organization of the scene. All computational mechanisms of the algorithm have direct neural correlates, and our results provide evidence for the interface theory of attention.

Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalVision Research
Volume94
DOIs
StatePublished - Jan 2014

Keywords

  • Attention
  • Gestalt
  • Proto-object
  • Saliency

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems

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