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Comparing rapid scene categorization of aerial and terrestrial views: A new perspective on scene gist
Author(s) -
Lester C. Loschky,
Ryan V. Ringer,
Katrina Ellis,
Bruce C. Hansen
Publication year - 2015
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/15.6.11
Subject(s) - categorization , perspective (graphical) , artificial intelligence , computer science , perception , computer vision , aerial image , psychology , image (mathematics) , neuroscience
Scene gist, a viewer's holistic representation of a scene from a single eye fixation, has been extensively studied for terrestrial views, but not for aerial views. We compared rapid scene categorization of both views in three experiments to determine the degree to which diagnostic information is view dependent versus view independent.We found large differences in observers' ability to rapidly categorize aerial and terrestrial scene views, consistent with the idea that scene gist recognition is viewpoint dependent.In addition, computational modeling showed that training models on one view (aerial or terrestrial) led to poor performance on the other view, thereby providing further evidence of viewpoint dependence as a function of available information. Importantly, we found that rapid categorization of terrestrial views (but not aerial views) was strongly interfered with by image rotation, further suggesting that terrestrial-view scene gist recognition is viewpoint dependent, with aerial-view scene recognition being viewpoint independent. Furthermore, rotation-invariant texture images synthesized from aerial views of scenes were twice as recognizable as those synthesized from terrestrial views of scenes (which were at chance), providing further evidence that diagnostic information for rapid scene categorization of aerial views is viewpoint invariant. We discuss the results within a perceptual-expertise framework that distinguishes between configural and featural processing, where terrestrial views are more effectively processed due to their predictable view-dependent configurations whereas aerial views are processed less effectively due to reliance on view-independent features.

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