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

Clicks: 377
ID: 82794
2019
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Abstract
In recent years, the use of a large number of object concepts and naturalistic object images has been growing strongly 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 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.
Reference Key
hebart2019thingsplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hebart, Martin N;Dickter, Adam H;Kidder, Alexis;Kwok, Wan Y;Corriveau, Anna;Van Wicklin, Caitlin;Baker, Chris I;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0223792
URL
Keywords
Deep learning pubmed one health ontology cbow module extraction snomed ct semantic deep learning skip-gram veterinary clinical narratives

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