The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images

  • Dietmar Heinke
  • Andreas Backhaus
  • Yarou Sun
  • Glyn W. Humphreys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)


We recently presented a computational model of object recognition and attention: the Selective Attention for Identification model (SAIM) [1,2,3,4,5,6,7]. SAIM was developed to model normal attention and attentional disorders by implementing translation-invariant object recognition in multiple object scenes. SAIM can simulate a wide range of experimental evidence on normal and disordered attention. In its earlier form, SAIM could only process black and white images. The present paper tackles this important shortcoming by extending SAIM with a biologically plausible feature extraction, using Gabor filters and coding colour information in HSV-colour space. With this extension SAIM proved able to select and recognize objects in natural multiple-object colour scenes. Moreover, this new version still mimicked human data on visual search tasks. These results stem from the competitive parallel interactions that characterize processing in SAIM.


Feature Extraction Visual Search Input Image Object Recognition Natural Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dietmar Heinke
    • 1
  • Andreas Backhaus
    • 1
  • Yarou Sun
    • 1
  • Glyn W. Humphreys
    • 1
  1. 1.Behavioural and Brain Sciences Centre, University of Birmingham, Birmingham B15 2TTUnited Kingdom

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