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Category systems for real-world scenes

Category systems for real-world scenes
Category systems for real-world scenes
Categorization performance is a popular metric of scene recognition and understanding in behavioral and computational research. However, categorical constructs and their labels can be somewhat arbitrary. Derived from exhaustive vocabularies of place names (e.g., Denget al., 2009), or the judgements of small groups of researchers (e.g., Fei-Fei, Iyer, Koch, & Perona, 2007),these categories may not correspond with human-preferred taxonomies. Here, we propose clustering by increasing the rand index via coordinate ascent (CIRCA): an unsupervised, data-driven clustering method for deriving ground-truth scene categories. In Experiment 1, human participants organized 80 stereoscopic images of outdoor scenes from the Southampton-York Natural Scenes (SYNS) dataset (Adams et al., 2016) into discrete categories. In separate tasks, images were grouped according to i) semantic content, ii) three-dimensional spatial structure, or iii) two-dimensional image appearance. Participants provided text labels for each group. Using the CIRCA method, we determined the most representative category structure and then derived category labels for each task/dimension. In Experiment 2, we found that these categories generalized well to a larger set of SYNS images, and new observers. In Experiment 3, we tested the relationship between our category systems and the spatial envelope model (Oliva & Torralba, 2001). Finally, in Experiment 4, we validated CIRCA on a larger, independent dataset of same-different category judgements. The derived category systems outperformed the SUN taxonomy (Xiao, Hays, Ehinger,Oliva, & Torralba, 2010) and an alternative clustering method (Greene, 2019). In summary, we believe this novel categorization method can be applied to a wide range of datasets to derive optimal categorical groupings and labels
Categorization, Category system, Clustering, Natural scenes, Real-world scene perception, taxonomy
1534-7362
1-31
Anderson, Matt D.
53946cbf-a70a-4782-ab28-12f3b9f34aa6
Graf, Erich
1a5123e2-8f05-4084-a6e6-837dcfc66209
Elder, James H.
f7d4f18e-09dd-4e5c-8fc9-b03064c9ff71
Ehinger, Krista
3738096b-076a-4137-964e-bdb8a163f9e8
Adams, Wendy
25685aaa-fc54-4d25-8d65-f35f4c5ab688
Anderson, Matt D.
53946cbf-a70a-4782-ab28-12f3b9f34aa6
Graf, Erich
1a5123e2-8f05-4084-a6e6-837dcfc66209
Elder, James H.
f7d4f18e-09dd-4e5c-8fc9-b03064c9ff71
Ehinger, Krista
3738096b-076a-4137-964e-bdb8a163f9e8
Adams, Wendy
25685aaa-fc54-4d25-8d65-f35f4c5ab688

Anderson, Matt D., Graf, Erich, Elder, James H., Ehinger, Krista and Adams, Wendy (2021) Category systems for real-world scenes. Journal of Vision, 21 (2), 1-31, [8]. (doi:10.1167/jov.21.2.8).

Record type: Article

Abstract

Categorization performance is a popular metric of scene recognition and understanding in behavioral and computational research. However, categorical constructs and their labels can be somewhat arbitrary. Derived from exhaustive vocabularies of place names (e.g., Denget al., 2009), or the judgements of small groups of researchers (e.g., Fei-Fei, Iyer, Koch, & Perona, 2007),these categories may not correspond with human-preferred taxonomies. Here, we propose clustering by increasing the rand index via coordinate ascent (CIRCA): an unsupervised, data-driven clustering method for deriving ground-truth scene categories. In Experiment 1, human participants organized 80 stereoscopic images of outdoor scenes from the Southampton-York Natural Scenes (SYNS) dataset (Adams et al., 2016) into discrete categories. In separate tasks, images were grouped according to i) semantic content, ii) three-dimensional spatial structure, or iii) two-dimensional image appearance. Participants provided text labels for each group. Using the CIRCA method, we determined the most representative category structure and then derived category labels for each task/dimension. In Experiment 2, we found that these categories generalized well to a larger set of SYNS images, and new observers. In Experiment 3, we tested the relationship between our category systems and the spatial envelope model (Oliva & Torralba, 2001). Finally, in Experiment 4, we validated CIRCA on a larger, independent dataset of same-different category judgements. The derived category systems outperformed the SUN taxonomy (Xiao, Hays, Ehinger,Oliva, & Torralba, 2010) and an alternative clustering method (Greene, 2019). In summary, we believe this novel categorization method can be applied to a wide range of datasets to derive optimal categorical groupings and labels

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Accepted/In Press date: 6 January 2021
Published date: 17 February 2021
Keywords: Categorization, Category system, Clustering, Natural scenes, Real-world scene perception, taxonomy

Identifiers

Local EPrints ID: 447240
URI: http://eprints.soton.ac.uk/id/eprint/447240
ISSN: 1534-7362
PURE UUID: b88be1dd-c2ca-4b01-b7ac-b3c61e4302b1
ORCID for Erich Graf: ORCID iD orcid.org/0000-0002-3162-4233
ORCID for Wendy Adams: ORCID iD orcid.org/0000-0002-5832-1056

Catalogue record

Date deposited: 05 Mar 2021 17:31
Last modified: 26 Nov 2021 02:48

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Contributors

Author: Erich Graf ORCID iD
Author: James H. Elder
Author: Krista Ehinger
Author: Wendy Adams ORCID iD

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