期刊
COGNITIVE PSYCHOLOGY
卷 58, 期 2, 页码 137-176出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cogpsych.2008.06.001
关键词
Scene recognition; Basic-level categorization; Global property; Natural images
资金
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0905017] Funding Source: National Science Foundation
- NIMH NIH HHS [R03 MH068322-01, R03 MH068322] Funding Source: Medline
Human observers are able to rapidly and accurately categorize natural scenes, but the representation mediating this feat is still unknown. Here we propose a framework of rapid scene categorization that does not segment a scene into objects and instead uses a vocabulary of global, ecological properties that describe spatial and functional aspects of scene space (such as navigability or mean depth). In Experiment 1, we obtained ground truth rankings on global properties for use in Experiments 2-4. To what extent do human observers use global property information when rapidly categorizing natural scenes? In Experiment 2, we found that global property resemblance was a strong predictor of both false alarm rates and reaction times in a rapid scene categorization experiment. To what extent is global property information alone a sufficient predictor of rapid natural scene categorization? In Experiment 3, we found that the performance of a classifier representing only these properties is indistinguishable from human performance in a rapid scene categorization task in terms of both accuracy and false alarms. To what extent is this high predictability unique to a global property representation? In Experiment 4, we compared two models that represent scene object information to human categorization performance and found that these models had lower fidelity at representing the patterns of performance than the global property model. These results provide support for the hypothesis that rapid categorization of natural scenes may not be mediated primarily though objects and parts, but also through global properties of structure and affordance. (c) 2008 Elsevier Inc. All rights reserved.
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