Journal article
How low can you go? Detecting style in extremely low resolution images
Journal of Experimental Psychology: Human Perception and Performance, Vol.45(5), pp.573-584
2019
Abstract
Humans can see through the complexity of scenes, faces, and objects by quickly extracting their redundant low-spatial and low-dimensional global properties, or their style. It remains unclear, however, whether semantic coding is necessary, or whether visual stylistic information is sufficient, for people to recognize and discriminate complex images and categories. In two experiments, we systematically reduce the resolution of hundreds of unique paintings, birds, and faces, and test people's ability to discriminate and recognize them. We show that the stylistic information retained at extremely low image resolutions is sufficient for visual recognition of images and visual discrimination of categories. Averaging over the 3 domains, people were able to reliably recognize images reduced down to a single pixel, with large differences from chance discriminability across 8 different image resolutions. People were also able to discriminate categories substantially above chance with an image resolution as low as 2 × 2 pixels. We situate our findings in the context of contemporary computational accounts of visual recognition and contend that explicit encoding of the local features in the image, or knowledge of the semantic category, is not necessary for recognizing and distinguishing complex visual stimuli.
Details
- Title
- How low can you go? Detecting style in extremely low resolution images
- Authors/Creators
- R.A. Searston (Author/Creator) - The University of AdelaideM.B. Thompson (Author/Creator) - Murdoch UniversityJ.R. Vokey (Author/Creator) - University of LethbridgeL.A. French (Author/Creator) - The University of QueenslandJ.M. Tangen (Author/Creator) - The University of Queensland
- Publication Details
- Journal of Experimental Psychology: Human Perception and Performance, Vol.45(5), pp.573-584
- Publisher
- American Psychological Association
- Identifiers
- 991005540026907891
- Copyright
- © 2019 American Psychological Association
- Murdoch Affiliation
- School of Allied Health
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 1 Clinical & Life Sciences
- 1.7 Neuroscanning
- 1.7.249 Visual Attention
- Web Of Science research areas
- Psychology
- Psychology, Experimental
- ESI research areas
- Psychiatry/Psychology