title: A model of proto-object based saliency.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, United States.
authors.forename: Alexander F
authors.initials: AF
authors.lastname: Russell
authors.affiliation: Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, United States; Zanvyl-Krieger Mind Brain Institute, Johns Hopkins University, Baltimore, MD 21218, United States.
authors.forename: Stefan
authors.initials: S
authors.lastname: Mihalaş
authors.affiliation: Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, United States; Zanvyl-Krieger Mind Brain Institute, Johns Hopkins University, Baltimore, MD 21218, United States.
authors.forename: Rudiger
authors.initials: R
authors.lastname: von der Heydt
authors.affiliation: Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, United States; Zanvyl-Krieger Mind Brain Institute, Johns Hopkins University, Baltimore, MD 21218, United States.
authors.forename: Ernst
authors.initials: E
authors.lastname: Niebur
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, United States. Electronic address: retienne@jhu.edu.
authors.forename: Ralph
authors.initials: R
authors.lastname: Etienne-Cummings
mesh_terms: D000328:Adult; D000465:Algorithms; D001288:Attention / Q000502:physiology*; D005133:Eye Movements / Q000502:physiology*; D005403:Fixation, Ocular / Q000502:physiology; D006801:Humans; D008962:Models, Theoretical; D010775:Photic Stimulation / Q000379:methods; D014796:Visual Perception / Q000502:physiology*
keywords: Attention; Gestalt; Proto-object; Saliency