Local statistics in natural scenes predict the saliency of synthetic textures Journal Article


Author(s): Tkačik, Gašper; Prentice, Jason S; Victor, Jonathan D; Balasubramanian, Vijay
Article Title: Local statistics in natural scenes predict the saliency of synthetic textures
Affiliation
Abstract: The visual system is challenged with extracting and representing behaviorally relevant information contained in natural inputs of great complexity and detail. This task begins in the sensory periphery: retinal receptive fields and circuits are matched to the first and second-order statistical structure of natural inputs. This matching enables the retina to remove stimulus components that are predictable (and therefore uninformative), and primarily transmit what is unpredictable (and therefore informative). Here we show that this design principle applies to more complex aspects of natural scenes, and to central visual processing. We do this by classifying high-order statistics of natural scenes according to whether they are uninformative vs. informative. We find that the uninformative ones are perceptually nonsalient, while the informative ones are highly salient, and correspond to previously identified perceptual mechanisms whose neural basis is likely central. Our results suggest that the principle of efficient coding not only accounts for filtering operations in the sensory periphery, but also shapes subsequent stages of sensory processing that are sensitive to high-order image statistics.
Keywords: natural scene statistics; psychophysics; vision
Journal Title: PNAS
Volume: 107
Issue 42
ISSN: 1091-6490
Publisher: National Academy of Sciences  
Date Published: 2010-10-19
Start Page: 18149
End Page: 18154
URL:
DOI: 10.1073/pnas.0914916107
Open access: no