• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Article

Salience models: a computational cognitive neuroscience review

MDPI Vision, Switzerland. 2019. Vol. 3. No. 4. P. 1-24.

The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model - so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely its contribution to our theoretical, neural and computational understanding of visual processing. Further, the model showed how salience could be used to make predictions for both spatial and temporal distributions of fixations. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modeling, many of which tried to augment the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks, however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modeling salience, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.