research-article

Toward Quantifying Ambiguities in Artistic Images

Authors:

Zoya Bylinskii,

Aaron Hertzmann,

Robert PepperellAuthors Info & Claims

ACM Transactions on Applied Perception (TAP), Volume 17, Issue 4

Article No.: 13, Pages 1 - 10

https://doi.org/10.1145/3418054

Published: 06 November 2020 Publication History

Abstract

It has long been hypothesized that perceptual ambiguities play an important role in aesthetic experience: A work with some ambiguity engages a viewer more than one that does not. However, current frameworks for testing this theory are limited by the availability of stimuli and data collection methods. This article presents an approach to measuring the perceptual ambiguity of a collection of images. Crowdworkers are asked to describe image content, after different viewing durations. Experiments are performed using images created with Generative Adversarial Networks, using the Artbreeder website. We show that text processing of viewer responses can provide a fine-grained way to measure and describe image ambiguities.

References

[1]

Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python. O’Reilly Media.

[2]

Tiberiu Boros, Stefan Daniel Dumitrescu, and Ruxandra Burtica. 2018. NLP-cube: End-to-end raw text processing with neural networks. In Proceedings of the CoNLL Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies. Association for Computational Linguistics, Brussels, Belgium, 171--179. Retrieved from http://www.aclweb.org/anthology/K18-2017.

[3]

Timothy F. Brady, Talia Konkle, George A. Alvarez, and Aude Oliva. 2008. Visual long-term memory has a massive storage capacity for object details. Proc. Natl. Acad. Sci. U.S.A. 105, 38 (2008), 14325--14329.

[4]

Timothy F. Brady, Talia Konkle, Jonathan Gill, Aude Oliva, and George A. Alvarez. 2013. Visual long-term memory has the same limit on fidelity as visual working memory. Psychol. Sci. 24, 6 (2013), 981--990.

[5]

Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. Large scale GAN training for high fidelity natural image synthesis. In Proceedings of the International Conference on Learning Representations (ICLR’19).

[6]

Claus-Christian Carbon. 2017. Art perception in the museum: How we spend time and space in art exhibitions. i-Percept. 8, 1 (2017), 2041669517694184.

[7]

Elizabeth Cowling. 2006. Visiting Picasso: The Notebooks and Letters of Roland Penrose. Thames 8 Hudson.

[8]

Nathaniel D. Daw and Aaron C. Courville. 2007. The Pigeon as particle filter. In Proceedings of the Conference on Neural Information Processing Systems (NIPS’07).

[9]

Scott L. Fairhall and Alumit Ishai. 2008. Neural correlates of object indeterminacy in art compositions. Conscious. Cogn. 17 (2008), 923--932.

[10]

Li Fei-Fei, Asha Iyer, Christof Koch, and Pietro Perona. 2007. What do we perceive in a glance of a real-world scene? J. Vision 7, 1 (01 2007), 10--10.

[11]

Camilo Fosco, Anelise Newman, Pat Sukhum, Yun Bin Zhang, Nanxuan Zhao, Aude Oliva, and Zoya Bylinskii. 2020. How much time do you have? Modeling multi-duration saliency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’20).

[12]

Dario Gamboni. 2004. Potential Images: Ambiguity and Indeterminacy in Modern Art. Reaktion Books.

[13]

Yotam Gingold, Ariel Shamir, and Daniel Cohen-Or. 2012. Micro perceptual human computation. ACM Trans. Graph. 31, 5, Article 119 (Aug. 2012), 12 pages.

Digital Library

[14]

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the Conference on Neural Information Processing Systems (NIPS’14).

[15]

Aaron Hertzmann. 2010. Non-photorealistic rendering and the science of art. In Proceedings of the International Symposium on Non-Photorealistic Animation and Rendering (NPAR’10).

Digital Library

[16]

Aaron Hertzmann. 2020. Visual indeterminacy in GAN art. Leonardo 53, 4 (2020).

[17]

Thomas Hofmann. 1999. Probabilistic latent semantic indexing. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99). 50--57.

Digital Library

[18]

Alumit Ishai, Scott L. Fairhall, and Robert Pepperell. 2007. Perception, memory and aesthetics of indeterminate art. Brain Res. Bull. 73, 4–6 (2007).

[19]

Martina Jakesch, Helmut Leder, and Michael Forster. 2013. Image ambiguity and fluency. PLoS ONE 8, 9 (2013).

[20]

Jan J. Koenderink, Andrea J. van Doorn, Astrid M. L. Kappers, and James T. Todd. 2001. Ambiguity and the ‘Mental Eye’ in pictorial relief. Perception 30, 4 (2001), 431--448.

[21]

Claudia Muth and Claus-Christian Carbon. 2013. The aesthetic aha: On the pleasure of having insights into Gestalt. Acta Psychologica 144, 1 (2013), 25--30.

[22]

Claudia Muth and Claus-Christian Carbon. 2016. SeIns: Semantic instability in art. Art Percept. 4, 1–2 (2016), 145--184.

[23]

Claudia Muth, Vera Hesslinger, and Claus-Christian Carbon. 2018. Variants of Semantic Instability (SeIns) in the arts. A classification study based on experiential reports. Psychol. Aesthet. Creativ. Arts 12, 1 (2018).

[24]

Claudia Muth, Vera M. Hesslinger, and Claus-Christian Carbon. 2015. The appeal of challenge in the perception of art: How ambiguity, solvability of ambiguity, and the opportunity for insight affect appreciation. Psychol. Aesthet. Creativ. Arts 9, 3 (2015).

[25]

Claudia Muth, Marius H. Raab, and Claus-Christian Carbon. 2016. Semantic stability is more pleasurable in unstable episodic contexts. On the relevance of perceptual challenge in art appreciation. Front. Hum. Neurosci. 10 (2016), 43 pages.

[26]

Aude Oliva. 2009. Visual scene perception. In Encyclopedia of Perception. SAGE Publications, Thousand Oaks, CA, 1112--1117.

[27]

Robert Pepperell. 2011. Connecting art and the brain: An artist’s perspective on visual indeterminacy. Front. Hum. Neurosci. 5 (2011), 84 pages.

[28]

Robert Pepperell. 2015. Artworks as dichotomous objects: Implications for the scientific study of aesthetic experience. Front. Hum. Neurosci. 9 (June 2015), 295 pages.

[29]

Mary C. Potter. 1975. Meaning in visual search. Science 187, 4180 (1975), 965--966.

[30]

Mary C. Potter. 1999. Understanding sentences and scenes: The role of conceptual short-term memory. Fleeting Memories: Cognition of Brief Visual Stimuli (1999). Bradford, UK, 13--46.

[31]

Gerhard Richter, Dietmar Elger, and Hans Ulrich Obrist. 2009. Gerhard Richter—Text: Writing, Interviews and Letters 1961–2007. Thames 8 Hudson, London.

[32]

Bryan Russell, Antonio Torralba, Kevin Murphy, and William T. Freeman. 2007. LabelMe: A database and web-based tool for image annotation. In Proceedings of the International Conference on Computer Vision (ICCV’07).

[33]

Philippe G. Schyns and Aude Oliva. 1994. From blobs to boundary edges: Evidence for time-and spatial-scale-dependent scene recognition. Psychol. Sci. 5, 4 (1994), 195--200.

[34]

Antonio Torralba. 2009. How many pixels make an image? Visual Neurosci. 26, 1 (2009), 123--131.

[35]

Sander Van de Cruys and Johan Wagemans. 2011. Putting reward in art: A tentative prediction error account of visual art. i-Percept. 2, 9 (2011).

[36]

C. Wallraven, K. Kaulard, C. Kürner, R. Pepperell, and H. Bülthoff. 2007. In the eye of the beholder - Perception of indeterminate art. In Proceedings of the 3rd Eurographics Conference on Computational Aesthetics in Graphics, Visualization, and Imaging. 121--128.

[37]

Christian Wallraven, Kathrin Kaulard, Cora Kürner, Robert Pepperell, and Heinrich H. Bülthoff. 2007. Psychophysics for perception of (in)determinate art. In Proceedings of the 4th Symposium on Applied Perception in Graphics and Visualization. 115--122.

Cited By

Hertzmann A(2024)Generative Models for the Psychology of Art and AestheticsEmpirical Studies of the Arts10.1177/02762374241288696Online publication date: 7-Oct-2024
https://doi.org/10.1177/02762374241288696
Geng DPark IOwens A(2024)Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02280(24154-24163)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02280
Geng DPark IOwens A(2024)Factorized Diffusion: Perceptual Illusions by Noise DecompositionComputer Vision – ECCV 202410.1007/978-3-031-72998-0_21(366-384)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72998-0_21
Show More Cited By

Index Terms

Toward Quantifying Ambiguities in Artistic Images
1. Applied computing
  1. Arts and humanities

Recommendations

Named entity recognition using point prediction and active learning
iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

Named entity recognition (NER) research has been spreading into specialty domains. A specialty domain corpus is smaller than a general domain corpus. Moreover, annotating a specialty domain corpus is more expensive than annotating a general corpus. ...
Canonicalizing Organization Names for Recruitment Domain
CoDS COMAD 2020: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD

Online recruitment industry relies on various Knowledge Bases (KB) for enabling search and recommendation systems. These KBs comprise of diverse, non-standard, and large volume of named-entities as they are created from vast unstructured user-generated ...
Creating Word Paintings Jointly Considering Semantics, Attention, and Aesthetics
In this article, we present a content-aware method for generating a word painting. Word painting is a composite artwork made from the assemblage of words extracted from a given text, which carries similar semantics and visual features to a given source ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Applied Perception

ACM Transactions on Applied Perception Volume 17, Issue 4

Special Issue on SAP 2020

October 2020

65 pages

ISSN:1544-3558

EISSN:1544-3965

DOI:10.1145/3434049

Editors:
Martin Giese
University of Tübingen, Germany
,
Victoria Interrante
University of Minnesota, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2020

Accepted: 01 August 2020

Received: 01 June 2020

Published in TAP Volume 17, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
246
Total Downloads

Downloads (Last 12 months)45
Downloads (Last 6 weeks)3

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hertzmann A(2024)Generative Models for the Psychology of Art and AestheticsEmpirical Studies of the Arts10.1177/02762374241288696Online publication date: 7-Oct-2024
https://doi.org/10.1177/02762374241288696
Geng DPark IOwens A(2024)Visual Anagrams: Generating Multi-View Optical Illusions with Diffusion Models2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02280(24154-24163)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02280
Geng DPark IOwens A(2024)Factorized Diffusion: Perceptual Illusions by Noise DecompositionComputer Vision – ECCV 202410.1007/978-3-031-72998-0_21(366-384)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-72998-0_21
Pepperell R(2023)Being alive to the world: an artist's perspective on predictive processingPhilosophical Transactions of the Royal Society B: Biological Sciences10.1098/rstb.2022.0429379:1895Online publication date: 18-Dec-2023
https://doi.org/10.1098/rstb.2022.0429
Frascaroli JLeder HBrattico EVan de Cruys S(2023)Aesthetics and predictive processing: grounds and prospects of a fruitful encounterPhilosophical Transactions of the Royal Society B: Biological Sciences10.1098/rstb.2022.0410379:1895Online publication date: 18-Dec-2023
https://doi.org/10.1098/rstb.2022.0410
Goetschalckx LAndonian AWagemans J(2021)Generative adversarial networks unlock new methods for cognitive scienceTrends in Cognitive Sciences10.1016/j.tics.2021.06.00625:9(788-801)Online publication date: Sep-2021
https://doi.org/10.1016/j.tics.2021.06.006

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents