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Graph-Based Visual Saliency

Harel, Jonathan and Koch, Christof and Perona, Pietro (2007) Graph-Based Visual Saliency. In: Advances in Neural Information Processing Systems 19 (NIPS 2006). Advances in Neural Information Processing Systems. No.19. MIT Press , Cambridge, MA, pp. 545-552. ISBN 0-262-19568-2.

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A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: first forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofar as it is naturally parallelized. This model powerfully predicts human fixations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84%.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Koch, Christof0000-0001-6482-8067
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2007 Massachusetts Institute of Technology. The authors express sincere gratitude to Wolfgang Einhäuser for his offering of natural images, and the fixation data associated with them from a study with seven human subjects. We also acknowledge NSF, NIH, DARPA, and ONR for their generous support of our research.
Group:Koch Laboratory (KLAB)
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Office of Naval Research (ONR)UNSPECIFIED
Series Name:Advances in Neural Information Processing Systems
Issue or Number:19
Record Number:CaltechAUTHORS:20160315-111145907
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65361
Deposited By: Kristin Buxton
Deposited On:30 Mar 2016 23:40
Last Modified:03 Oct 2019 09:46

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