A saliency-based bottom-up visual attention model for dynamic scenes analysis

David F. Ramirez-Moreno, Odelia Schwartz, Juan F. Ramirez-Villegas

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work adds motion saliency calculations to a neural network model with realistic temporal dynamics [(e.g., building motion salience on top of De Brecht and Saiki Neural Networks 19:1467-1474, (2006)]. The resulting network elicits strong transient responses to moving objects and reaches stability within a biologically plausible time interval. The responses are statistically different comparing between earlier and later motion neural activity; and between moving and non-moving objects. We demonstrate the network on a number of synthetic and real dynamical movie examples. We show that the model captures the motion saliency asymmetry phenomenon. In addition, the motion salience computation enables sudden-onset moving objects that are less salient in the static scene to rise above others. Finally, we include strong consideration for the neural latencies, the Lyapunov stability, and the neural properties being reproduced by the model.

Original languageEnglish (US)
Pages (from-to)141-160
Number of pages20
JournalBiological Cybernetics
Issue number2
StatePublished - Apr 2013
Externally publishedYes


  • Asymmetry phenomenon
  • Lyapunov stability
  • Motion saliency
  • Neural latency
  • Neural network
  • Saliency map
  • Synaptic depression
  • Visual attention

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)


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