Recognition of natural scenes from global properties: seeing the forest without representing the trees. Greene, M. R & Oliva, A. Cogn Psychol, 58(2):137-76, 2009. doi abstract bibtex 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.
@Article{Greene2009,
author = {Michelle R Greene and Aude Oliva},
journal = {Cogn Psychol},
title = {Recognition of natural scenes from global properties: seeing the forest without representing the trees.},
year = {2009},
number = {2},
pages = {137-76},
volume = {58},
abstract = {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.},
doi = {10.1016/j.cogpsych.2008.06.001},
keywords = {Adolescent, Adult, Female, Humans, Male, Models, Pattern Recognition, Psychological, Reaction Time, Visual, 18762289},
}
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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.","doi":"10.1016/j.cogpsych.2008.06.001","keywords":"Adolescent, Adult, Female, Humans, Male, Models, Pattern Recognition, Psychological, Reaction Time, Visual, 18762289","bibtex":"@Article{Greene2009,\n author = {Michelle R Greene and Aude Oliva},\n journal = {Cogn Psychol},\n title = {Recognition of natural scenes from global properties: seeing the forest without representing the trees.},\n year = {2009},\n number = {2},\n pages = {137-76},\n volume = {58},\n abstract = {Human observers are able to rapidly and accurately categorize natural\n\tscenes, but the representation mediating this feat is still unknown.\n\tHere we propose a framework of rapid scene categorization that does\n\tnot segment a scene into objects and instead uses a vocabulary of\n\tglobal, ecological properties that describe spatial and functional\n\taspects of scene space (such as navigability or mean depth). In Experiment\n\t1, we obtained ground truth rankings on global properties for use\n\tin Experiments 2-4. To what extent do human observers use global\n\tproperty information when rapidly categorizing natural scenes? In\n\tExperiment 2, we found that global property resemblance was a strong\n\tpredictor of both false alarm rates and reaction times in a rapid\n\tscene categorization experiment. To what extent is global property\n\tinformation alone a sufficient predictor of rapid natural scene categorization?\n\tIn Experiment 3, we found that the performance of a classifier representing\n\tonly these properties is indistinguishable from human performance\n\tin a rapid scene categorization task in terms of both accuracy and\n\tfalse alarms. To what extent is this high predictability unique to\n\ta global property representation? In Experiment 4, we compared two\n\tmodels that represent scene object information to human categorization\n\tperformance and found that these models had lower fidelity at representing\n\tthe patterns of performance than the global property model. These\n\tresults provide support for the hypothesis that rapid categorization\n\tof natural scenes may not be mediated primarily though objects and\n\tparts, but also through global properties of structure and affordance.},\n doi = {10.1016/j.cogpsych.2008.06.001},\n keywords = {Adolescent, Adult, Female, Humans, Male, Models, Pattern Recognition, Psychological, Reaction Time, Visual, 18762289},\n}\n\n","author_short":["Greene, M. 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