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THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images

View ORCID ProfileMartin N Hebart, Adam H Dickter, Alexis Kidder, Wan Y Kwok, Anna Corriveau, Caitlin van Wicklin, View ORCID ProfileChris I Baker
doi: https://doi.org/10.1101/545954
Martin N Hebart
National Institute of Mental Health
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  • For correspondence: martin.hebart@gmail.com
Adam H Dickter
National Institute of Mental Health
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  • For correspondence: adam.dickter@nih.gov
Alexis Kidder
National Institute of Mental Health
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  • For correspondence: alexis.kidder@nih.gov
Wan Y Kwok
National Institute of Mental Health
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  • For correspondence: wan.kwok@nih.gov
Anna Corriveau
National Institute of Mental Health
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  • For correspondence: anna.corriveau@nih.gov
Caitlin van Wicklin
National Institute of Mental Health
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  • For correspondence: caitv95@vt.edu
Chris I Baker
National Institute of Mental Health
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  • For correspondence: bakerchris@mail.nih.gov
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Abstract

In recent years, the use of a large number of object concepts and naturalistic object images has been growing enormously in cognitive neuroscience research. Classical databases of object concepts are based mostly on a manually-curated set of concepts. Further, databases of naturalistic object images typically consist of single images of objects cropped from their background, or a large number of uncontrolled naturalistic images of varying quality, requiring elaborate manual image curation. Here we provide a set of 1,854 diverse object concepts sampled systematically from concrete picturable and nameable nouns in the American English language. Using these object concepts, we conducted a large-scale web image search to compile a database of 26,107 high-quality naturalistic images of those objects, with 12 or more object images per concept and all images cropped to square size. Using crowdsourcing, we provide higher-level category membership for the 27 most common categories and validate them by relating them to representations in a semantic embedding derived from large text corpora. Finally, by feeding images through a deep convolutional neural network, we demonstrate that they exhibit high selectivity for different object concepts, while at the same time preserving variability of different object images within each concept. Together, the THINGS database provides a rich resource of object concepts and object images and offers a tool for both systematic and large-scale naturalistic research in the fields of psychology, neuroscience, and computer science.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CC0 license.
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Posted February 10, 2019.
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THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images
Martin N Hebart, Adam H Dickter, Alexis Kidder, Wan Y Kwok, Anna Corriveau, Caitlin van Wicklin, Chris I Baker
bioRxiv 545954; doi: https://doi.org/10.1101/545954
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THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images
Martin N Hebart, Adam H Dickter, Alexis Kidder, Wan Y Kwok, Anna Corriveau, Caitlin van Wicklin, Chris I Baker
bioRxiv 545954; doi: https://doi.org/10.1101/545954

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